{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "os.environ[\"PYSPARK_PYTHON\"] = \"C:/Users/mars/Anaconda3/envs/name1/python.exe\"\n",
    "os.environ[\"JAVA_HOME\"] = \"c:/jdk\"\n",
    "os.environ[\"SPARK_HOME\"] = \"c:/spark\"\n",
    "os.environ[\"PYLIB\"] = os.environ[\"SPARK_HOME\"] + \"/python/lib\"\n",
    "sys.path.insert(0, os.environ[\"PYLIB\"] +\"/py4j-0.10.7-src.zip\")\n",
    "sys.path.insert(0, os.environ[\"PYLIB\"] +\"/pyspark.zip\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import pyspark\n",
    "from pyspark import SparkContext, SparkConf\n",
    "conf = SparkConf().setAppName(\"sajad\").setMaster(\"local\")\n",
    "sc = SparkContext(conf=conf)\n",
    "\n",
    "from pyspark.sql import SparkSession\n",
    "from pyspark.sql import SparkSession\n",
    "\n",
    "spark = SparkSession \\\n",
    "    .builder \\\n",
    "    .appName(\"Python Spark SQL basic example\") \\\n",
    "    .config(\"spark.some.config.option\", \"some-value\") \\\n",
    "    .getOrCreate()\n",
    "\n",
    "\n",
    "from pyspark.sql.functions import explode\n",
    "from pyspark.sql.functions import split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pyspark.sql.dataframe.DataFrame"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_O = spark.read.load('creditcard.csv', \n",
    "                          format='csv', \n",
    "                          header='true', \n",
    "                          inferSchema='true')\n",
    "\n",
    "type(data_O)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-----+------+\n",
      "|Class| count|\n",
      "+-----+------+\n",
      "|    1|   492|\n",
      "|    0|284315|\n",
      "+-----+------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "classFreq = data_O.groupBy(\"Class\").count()\n",
    "classFreq.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(284807, 31)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "data= data_O.toPandas()\n",
    "data= data.sample(frac=1)\n",
    "data.sample(frac=1).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2848, 31)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.sample(frac=0.01).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(984, 31)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "data= data_O.toPandas()\n",
    "data= data.sample(frac=1)\n",
    "\n",
    "# amount of fraud classes 492 rows.\n",
    "fraud_df = data.loc[data['Class'] == 1]\n",
    "non_fraud_df = data.loc[data['Class'] == 0][:492]\n",
    "\n",
    "normal_distributed_df = pd.concat([fraud_df, non_fraud_df])\n",
    "\n",
    "# Shuffle dataframe rows\n",
    "new_df = normal_distributed_df.sample(frac=1, random_state=42)\n",
    "\n",
    "new_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Distribution of the Classes in the subsample dataset\n",
      "1    492\n",
      "0    492\n",
      "Name: Class, dtype: int64\n",
      "1    0.5\n",
      "0    0.5\n",
      "Name: Class, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "print('Distribution of the Classes in the subsample dataset')\n",
    "print(new_df['Class'].value_counts())\n",
    "print(new_df['Class'].value_counts()/len(new_df))\n",
    "\n",
    "\n",
    "\n",
    "sns.countplot('Class', data=new_df)\n",
    "plt.title('Equally Distributed Classes', fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9aaDPA4Fbhvr8T9vnsIF+N00l5VrXAXeiqZz7waw8wczeBbwvyfOAC4GPDSTpHg48Nsnmac57ANMk5mbwQODyqaRc67M07+Ywbk0GXrEDSTmAm4HLpnaS3AO4L/DeJP8w0G9PmoTqTB4O7At8vyme22IfmuebjftdPvWlqm5O8n3gV9qmh9G8l/9meg8HjmqTcVMmaP4e78WtieKftvfdh6YqUZIkaU4xMSdJ0u5tKnFyN25Nhmwr4TNlW31q4PtwldPUsZ2dLuOWaWK401Y3qnpdktNpFg9YCPx1khdX1fL2/h+nWc102HdvRxxh6+fdKoSB7z/ewevd1M6tNmXqPb2Y2zesd4LmOY6e5tiPZul+Px/ar4Hzt/c3NAG8HvjoNMcGF9W4O3BjVZmUkyRJc5KJOUmSdm9fp0nUHAZc2bZdTZN0eSRwDWyZM+zB3Doc8Uqa5MqjaOYuI8mdaeYSmxqeeEf8jGZ45fZ8n2ao5qDhfarqKuAq4O/aCrAX0sz/dinwNGBDVQ0nmG6PK4Hjk/zyQNXcb9K8m6/sxHUBqKrvJvk28ICqOu12nHopcE/gltuz2u5O3G+6+z87yf4zVM1dChw6MNR3Jg9u+0qSJM1JLv4gSdJurKpuoVk44DEDbZuBU4C3JnlimpVWlzOQMGsTXufSDHk8OslDgA/RJPk+vBMhXQs8OMmvJ9k/yZ1m6PevwMOSHJ/k4CSvBB49dTDJvCTvSfL4JPOT/Eb7jFPJx/cAdwHOTPIbSe6f5Lh2FdNfvh3xnk5TDXdamtVZHwu8Fzh7B5JOO+p1wCvblVF/PcmDkzw3yWu2cc5q4N+Ac5M8Kcn9kjwqyeuTTFdFt7P3G/Zh4HvAOe3fx/2S/M7AqqxvAJ6Z5A3t9Q9N8gdJ3jZ0naNpFteQJEmak0zMSZK0+1sGPD3JYKXaK4A1wMr28wrayrgBL6CZi+289nNfYNHAYgN3xD/RVJqtp6mKe/R0ndpFKV4PvBn4As3qnycPdPkFzfDcD9CspLqSZj68l7fnX9de+xaaxM+XaZJ1N7XbDqmqn9AMk70zzTs4t73P8Tt6jR24x/va6z0H+CJwEbAE+MY2zingt2gSmP9E8w4+Avw6zTx/s3q/aa7xY5rVVL8N/DPN+3097fDe9vd7MnAMzXtbB7yaZvEQAJLch6b6cGcqMCVJknZpuXVRLEmStLtKcglwclV9sOtYJIAkbwfuUlVLuo5FkiSpK72omEuyNsnCobalSU5Ocn6S65Os6io+SZJ2Ay+iJ//dl1rfA/6q6yAkSZK61IuKuSQvAh5ZVS8YaPsccCKwF83QmRdV1eKOQpQkSZIkSZJmVV/+z/lZwOIkewMkmQ8cAFxcVRcCN8x8qiRJkiRJkrTr6UVirqo20UwKvKhtmgTOrD6U80mSJEmSJEkjsGfXAQxYQZOQO7f9vF2rnSVZQrOiGA94yJ89/F4H/fasB3hHrV66oesQJEmSJEkau59c8m9dh7DFvPvP7zqE25j3zNek6xj64jG//emRFmdd/M+P6+W77kXFXOsc4NgkRwLzqurS23NyVS2rqgVVtaBPSTlJkiRJkiRpOr2pmKuqzUnWAstpquckSZIkSZI0B2SiT7Vj49O3p14BHA6cMdWQ5CLgozTVdBuTLOwqOEmSJEmSJGm29KZiDqCqVgIZaju6o3AkSZIkSZI0Bpno5RRwI9e3ijlJkiRJkiRpTuhVxZwkSZIkSZLmnmRu1o7NzaeWJEmSJEmSOrZbVsytXrqh6xC2ctxJB3UdwhZ9ezeSJEmSpN3XJa9f23UIWxz97+/uOoTbmNd1AD3iHHOSJEmSJEmSxma3rJiTJEmSJEnSriMTc7N2bG4+tSRJkiRJktQxK+YkSZIkSZLUqQnnmOtOkrVJFg61LU3yiSSXJPlyksuTPL2rGCVJkiRJkqTZ1JeKuRXAJHDBQNsk8Crguqq6KskBwBeSXFBV13cRpCRJkiRJkmZf0ovasbHry1OfBSxOsjdAkvnAAcBnquoqgKq6DvgecI+OYpQkSZIkSZJmTS8q5qpqU5J1wCLgXJpquTOrqqb6JDkK2Av4ejdRSpIkSZIkaRTiHHOdmxrOSvu5YupAknsDHwReUFW3THdykiVJ1idZf8qqNSMPVpIkSZIkSbMjExMj3fqqFxVzrXOAdyU5EphXVZcCJLkz8HHgL6vqczOdXFXLgGUAN154Ws3UT5IkSZIkSeqD3iTmqmpzkrXActpquSR7ASuB06rqox2GJ0mSJEmSpBFxKGs/rAAOB85o958GPBZ4fpLL2u2IzqKTJEmSJEmSZklvKuYAqmolkIH9DwEf6i4iSZIkSZIkjdpE+lY7Nh5z86klSZIkSZKkjvWqYk6SJEmSJElzj3PMSZIkSZIkSRobK+bGYPXSDV2HsMVxJx3UdQhb6dO7kSRJkiTNrke/6Yldh3CrtR/vOoLbOrJH76djVsxJkiRJkiRJGhsr5iRJkiRJktSpuCqrJEmSJEmSpHExMSdJkiRJkqROZSIj3XYohmRRkq8muTrJq6c5flCSC5NcnmRtkgN39rl7kZhrH2bhUNvSJO9P8oUklyX5cpIXdxWjJEmSJEmSdk9J9gDeAzwJOAx4RpLDhrq9Azitqh4KvAF4y87ety9zzK0AJoELBtomgVcBn6uqm5LsB1yR5Lyquq6LICVJkiRJkjT7JiY6rx07Cri6qq4BSHIG8BTgyoE+hwEva7+vAc7Z2Zt2/tSts4DFSfYGSDIfOAD4TFXd1PbZm/7EK0mSJEmSpF1EkiVJ1g9sS4a63Af41sD+xrZt0BeB32+//y7wy0n+187E1YuKuaralGQdsAg4l6Za7syqqiT3BT4OHAycaLWcJEmSJEnS7iXZsXng7qiqWgYs21YI0502tP8K4N1Jng98Bvg2cPPOxNWnCrSp4ay0nysAqupb7djdg4HnJbnndCcPZj5PWbVmLAFLkiRJkiRpt7ARuO/A/oHAVsVhVXVdVf1eVT0M+Iu27X925qZ9SsydAxyb5EhgXlVdOniwrZT7MnD0dCdX1bKqWlBVC05YfMzoo5UkSZIkSdKs6MGqrJ8HDklyvyR70RSNnbdVjMn+SaZyaa8Blu/sc/cmMVdVm4G1NA+1AiDJgUnmtd/vBjwa+GpXMUqSJEmSJGn3U1U3Ay+hWZj0K8BHqurLSd6Q5Hfabo8Hvprka8A9gTfv7H17McfcgBXA2dw6pPWBwDuTFM1Y33dU1Ze6Ck6SJEmSJEmzbwer2kaqqj4BfGKo7bUD38+iWcB01vQqMVdVKxmYbK+qPgU8tLuIJEmSJEmSpNHoVWJOkiRJkiRJc89EejPb2ljNzaeWJEmSJEmSOmbFnCRJkiRJkjrVhznmumBibo5ZvXRD1yFs5biTDuo6hK307f1IkiRJ0q4sT3xq1yFs8fO99u06BOk2TMxJkiRJkiSpU1bMSZIkSZIkSR1I5mZizsUfJEmSJEmSpA5YMSdJkiRJkqROTczRoaxWzEmSJEmSJEkd6EViLsnaJAuH2pYmObn9fuck307y7m4ilCRJkiRJ0qhkYmKkW1/1JbIVwORQ22TbDvBG4NNjjUiSJEmSJEkaob4k5s4CFifZGyDJfOAA4OIkDwfuCXyys+gkSZIkSZI0MklGuvVVLxJzVbUJWAcsapsmgTOBAO8ETtzeNZIsSbI+yfpTVq0ZWaySJEmSJEnSbOjTqqxTw1nPbT+PB/4Y+ERVfWt72c2qWgYsA7jxwtNqtKFKkiRJkiRptmSOrsrap8TcOcC7khwJzKuqS5P8GXB0kj8G9gP2SrK5ql7daaSSJEmSJEnSTupNYq6qNidZCyynXfShqp41dTzJ84EFJuUkSZIkSZJ2LxNztGKuF3PMDVgBHA6c0XUgkiRJkiRJ0ij1pmIOoKpW0iz4MN2xU4FTxxmPJEmSJEmSRq/PK6eOUt8q5iRJkiRJkqQ5oVcVc5IkSZIkSZp7XJVV6sDqpRu6DmErx510UNchbNG3dyNJkiRJt9e33/iWrkPY4v5P+/+6DuG2HvybXUegjpmYkyRJkiRJUqcmnGNOkiRJkiRJ0rhYMSdJkiRJkqROzdU55qyYkyRJkiRJkjpgxZwkSZIkSZI6ZcVch5KsTbJwqG1pkpOT/CLJZe12XlcxSpIkSZIkSbOpLxVzK4BJ4IKBtkngROC5VXVEJ1FJkiRJkiRp5OKqrJ06C1icZG+AJPOBA4CLO4xJkiRJkiRJGpleJOaqahOwDljUNk0CZ1ZVAfskWZ/kc0meOtM1kixp+60/ZdWaMUQtSZIkSZKk2TAxkZFufdWXoaxw63DWc9vP49v2X62q65LcH/jXJF+qqq8Pn1xVy4BlADdeeFqNKWZJkiRJkiTpDulFxVzrHODYJEcC86rqUoCquq79vAZYCzysswglSZIkSZI065KMdOur3iTmqmozTeJtOU31HEnuNjDv3P7Ao4Eru4pRkiRJkiRJmi19GsoKTULubJqhrAAPBN6b5BaaJOLfVJWJOUmSJEmSpN1IejwP3Cj1KjFXVSuBDOx/FnhIdxFJkiRJkiRp1OZoXq4/Q1klSZIkSZKkuaRXFXOSJEmSJEmaexzKKonVSzd0HcIWx510UNchbKVP70aSJEnSruGX3n5y1yHc6j//tesIpNswMSdJkiRJkqROzdWKOeeYkyRJkiRJkjpgxZwkSZIkSZI6NREr5iRJkiRJkiSNiRVzkiRJkiRJ6pRzzHUoydokC4faliY5OcmvJvlkkq8kuTLJ/G6ilCRJkiRJkmZPLxJzwApgcqhtsm0/DXh7VT0QOAr43phjkyRJkiRJ0gglo936qi+JubOAxUn2Bmir4g4AfgDsWVWfAqiqzVX1k66ClCRJkiRJkmZLLxJzVbUJWAcsapsmgTOBQ4Drk5yd5D+SvD3JHtNdI8mSJOuTrD9l1ZrxBC5JkiRJkqSdNjGRkW591YvEXGtwOOvUMNY9gaOBVwCPAO4PPH+6k6tqWVUtqKoFJyw+ZvTRSpIkSZIkSTuhT4m5c4BjkxwJzKuqS4GNwH9U1TVVdXPb58gug5QkSZIkSdLsSjLSra96k5irqs3AWmA5TbUcwOeBuyW5R7v/BODK8UcnSZIkSZIkza49uw5gyArgbNohrVX1iySvAC5Mk978AvBPHcYnSZIkSZKkWTbR46q2UepVYq6qVgIZavsU8NBuIpIkSZIkSZJGo1eJOUmSJEmSJM096c1ka+M1Rx9bkiRJkiRJ6pYVc1JPrV66oesQtnLcSQd1HcJW+vZ+JEmSJN3WNT/91a5D2OJuV1/VdQi3sU/XAfRIn1dOHSUr5iRJkiRJkqQOWDEnSZIkSZKkTk1MWDEnSZIkSZIkaUysmJMkSZIkSVKn5ugUc1bMSZIkSZIkSV3oRWIuydokC4falib5SpLLBrYbkzy1qzglSZIkSZI0+zKRkW591ZehrCuASeCCgbZJYElVXQSQ5O7A1cAnxx+eJEmSJEmSRqXHubOR6kXFHHAWsDjJ3gBJ5gMHABcP9PkD4F+q6idjj06SJEmSJEmaZb1IzFXVJmAdsKhtmgTOrKoa6DZJU1k3rSRLkqxPsv6UVWtGF6wkSZIkSZJmVZKRbn3Vi8Rca2o4Kwwl4ZLcG3gIWw913UpVLauqBVW14ITFx4w0UEmSJEmSJGln9WWOOYBzgHclORKYV1WXDhx7GrCyqn7eTWiSJEmSJEkalYk+lY6NUW8eu6o2A2uB5dx2yOozpmmTJEmSJEmSdll9qpiDJvl2NrcOaZ1aCOK+wKe7CUmSJEmSJEmj1Od54EapV4m5qloJZKjtWuA+nQQkSZIkSZIkjUivEnOSJEmSJEmae9KbydbGa44+tiRJkiRJktQtK+Yk7ZDVSzd0HcJWjjvpoK5D2Erf3o8kSZLUB4/80kldh7BFPeyorkPQNkzM0TnmrJiTJEmSJEmSOmDFnCRJkiRJkjo1RwvmrJiTJEmSJEmSumDFnCRJkiRJkjplxZwkSZIkSZKkselFxVyStcBbquqCgbalwK8Bm4En0yQRPwW8tKqqizglSZIkSZI0+yYm5mbJXF8q5lYAk0Ntk8CZwKOBhwIPBh4BPG68oUmSJEmSJEmzry+JubOAxUn2BpUTw0oAACAASURBVEgyHzgA+BmwD7AXsDdwJ+C73YQoSZIkSZKkUUhGu/VVLxJzVbUJWAcsapsmgTOr6hJgDfCddrugqr4y3TWSLEmyPsn6U1atGUfYkiRJkiRJ0h3WiznmWlPDWc9tP49PcjDwQODAts+nkjy2qj4zfHJVLQOWAdx44WnOQSdJkiRJkrSLmKNTzPWjYq51DnBskiOBeVV1KfC7wOeqanNVbQb+BXhkl0FKkiRJkiRJs6E3ibk28bYWWE5TPQfwTeBxSfZMcieahR+mHcoqSZIkSZKkXVMmMtKtr3qTmGutAA4Hzmj3zwK+DnwJ+CLwxar6545ikyRJkiRJkmZNn+aYo6pWAhnY/wXwou4ikiRJkiRJ0qj1eeXUUepbxZwkSZIkSZI0dkkWJflqkquTvHqGPk9LcmWSLyf58M7es1cVc5IkSZIkSZp7up4GLskewHuAJwIbgc8nOa+qrhzocwjwGuDRVfXDJL+ys/c1MSdpl7R66YauQ9jKcScd1HUIW/Tt3UiSJGnuyp3v2nUIW+Q6/52sbToKuLqqrgFIcgbwFODKgT5/CLynqn4IUFXf29mbOpRVkiRJkiRJnUpGu+2A+wDfGtjf2LYN+jXg15L8W5LPJVm0s89txZwkSZIkSZI6NTHi0rEkS4AlA03LqmrZYJdpTquh/T2BQ4DHAwcCFyV5cFVdf0fjMjEnSZIkSZKk3VqbhFu2jS4bgfsO7B8IXDdNn89V1c+BbyT5Kk2i7vN3NC6HskqSJEmSJKlTPRjK+nngkCT3S7IXMAmcN9TnHOCYJt7sTzO09ZqdeW4Tc5IkSZIkSZrTqupm4CXABcBXgI9U1ZeTvCHJ77TdLgA2JbkSWAOcWFWbdua+vRjKmmQt8JaqumCgbSlN5vEG4Mlt8xur6szxRyhJkiRJkqRRyQ6WtY1SVX0C+MRQ22sHvhfw8nabFX2pmFtBUyI4aBL4LnAkcATwG8CJSe485tgkSZIkSZKkWdeXxNxZwOIkewMkmQ8cAPwE+HRV3VxVPwa+COz0UrSSJEmSJEnqj4mMduurXiTm2vG467g16TYJnEmTiHtSkn3bSfWOYesVMrZIsiTJ+iTrT1m1ZhxhS5IkSZIkSXdYL+aYa00NZz23/Ty+qi5N8gjgs8D3gUuAm6c7eXDZ2xsvPK3GErEkSZIkSZJ2Wg+mmOtELyrmWucAxyY5EphXVZcCVNWbq+qIqnoiEOCqLoOUJEmSJEmSZkNvKuaqanO7Outymuo5kuwB3LWqNiV5KPBQ4JPdRSlJkiRJkqTZNlcr5nqTmGutAM7m1hVa7wRc1C6Z+yPg2VU17VBWSZIkSZIkaVfSq8RcVa2kGa46tX8jcFh3EUmSJEmSJGnUJvo02doYzdHHliRJkiRJkrrVq4o5SZIkSZIkzT3OMSdJusNWL93QdQhbHHfSQV2HsJU+vRtJkiSNV/3o+q5D2OLGDd/qOoTb2KfrANQ5E3OSJEmSJEnq1MQcrZhzjjlJkiRJkiSpA1bMSZIkSZIkqVNzdY45K+YkSZIkSZKkDlgxJ0mSJEmSpE5ZMTcGSdYmWTjUtjTJyUnOT3J9klVDx++X5N+TXJXkzCR7jTNmSZIkSZIkaRTGPZR1BTA51DbZtr8deM4057wV+NuqOgT4IXDCSCOUJEmSJEnSWE1ktFtfjTsxdxawOMneAEnmAwcAF1fVhcANg52TBHhCex7AB4CnjitYSZIkSZIkaVTGmpirqk3AOmBR2zQJnFlVNcMp/wu4vqpubvc3AveZrmOSJUnWJ1l/yqo1sxm2JEmSJEmSRigZ7dZXXazKOjicdWoY60yme3XTJvGqallVLaiqBScsPmYnQ5QkSZIkSZJGq4tVWc8B3pXkSGBeVV26jb7/Ddw1yZ5t1dyBwHXjCFKSJEmSJEnjkcw0mHLW7jDi698xY6+Yq6rNwFpgOduulqMd4roG+IO26XnAuaOMT5IkSZIkSePl4g/jtQI4HDhjqiHJRcBHgWOTbEyysD30KuDlSa6mmXPulHEHK0mSJEmSJM22LoayUlUrGaohrKqjZ+h7DXDUOOKSJEmSJEnS+PV5gYZR6qpiTpIkSZIkSZrTOqmYkyRJkiRJkqbM1Yo5E3OStJtZvXRD1yFs5biTDuo6hK307f1IkiTtznLP+3QdwhY3H3Fs1yFIt2FiTpIkSZIkSZ2aSI34Dv0syXOOOUmSJEmSJKkDVsxJkiRJkiSpU3N1jjkr5iRJkiRJkqQOWDEnSZIkSZKkTlkxNwZJ1iZZONS2NMnJSc5Pcn2SVUPHX5Lk6iSVZP9xxitJkiRJkiSNyrgr5lYAk8AFA22TwInAXsC+wIuGzvk3YBWwdgzxSZIkSZIkacwmrJgbi7OAxUn2BkgyHzgAuLiqLgRuGD6hqv6jqq4dY4ySJEmSJEnSyI01MVdVm4B1wKK2aRI4s6pqZ6+dZEmS9UnWn7Jqzc5eTpIkSZIkSWMSaqRbX3WxKuvUcFbazxWzcdGqWlZVC6pqwQmLj5mNS0qSJEmSJEkj00Vi7hzg2CRHAvOq6tIOYpAkSZIkSVJPJKPd+mrsibmq2kyzkMNyZqlaTpIkSZIkSdrVdFExB01C7nDgjKmGJBcBH6WpptuYZGHb/qdJNgIHApcneV8XAUuSJEmSJGk0JjLara/27OKmVbUSyFDb0TP0/Tvg78YRlyRJkiRJkjQunSTmJEmSJEmSpClJf1dOHaWuhrJKkiRJkiRJc5oVc5KkkVq9dEPXIWzluJMO6jqELfr2biRJkmbdLbd0HcEWdfrJXYdwW28+qusIeqPPK6eOkhVzkiRJkiRJUgesmJMkSZIkSVKnJnCOOUmSJEmSJEljYsWcJEmSJEmSOjVX55gzMSdJkiRJkqROJQ5lHbkka5MsHGpbmuTkJOcnuT7JqqHjpyf5apIrkixPcqdxxixJkiRJkiSNwrjnmFsBTA61TbbtbweeM805pwOHAg8B5gEvHGWAkiRJkiRJGq+JjHbrq3En5s4CFifZGyDJfOAA4OKquhC4YfiEqvpEtYB1wIHjC1eSJEmSJEkajbEm5qpqE01ybVHbNAmc2Sbdtqkdwvoc4PwZji9Jsj7J+lNWrZmtkCVJkiRJkjRioUa69dW4K+Zg6+GsU8NYd8TJwGeq6qLpDlbVsqpaUFULTlh8zCyEKUmSJEmSJI1OF6uyngO8K8mRwLyqunR7JyT5a+AewItGHZwkSZIkSZLGKz2eB26Uxp6Yq6rNSdYCy9mBarkkLwQWAsdW1S0jDk+SJEmSJEkaiy6GskKTkDscOGOqIclFwEeBY5NsTLKwPfSPwD2BS5JcluS1Y49WkiRJkiRJI5PUSLe+6mIoK1W1EshQ29Ez9O0kRkmSJEmSJGmUTHpJkiRJkiSpUxM9Xjl1lLoayipJkiRJkiTNaVbMSZLmlNVLN3QdwhbHnXRQ1yFspU/vRpIk7R5qjz26DmGLOz/hCV2HoG2Yq6uyWjEnSZIkSZIkdcCKOUmSJEmSJHWqzyunjpIVc5IkSZIkSVIHrJiTJEmSJElSp+KqrJIkSZIkSZLGZayJuSRrkywcalua5OQk5ye5PsmqoeOnJPliksuTnJVkv3HGLEmSJEmSpNGayGi3vhp3xdwKYHKobbJtfzvwnGnOeVlVHV5VDwW+CbxktCFKkiRJkiRJozfuxNxZwOIkewMkmQ8cAFxcVRcCNwyfUFU/avsGmAdzdNCxJEmSJEnSbirUSLe+Gmtirqo2AeuARW3TJHBmVW3zDSV5P/BfwKHA38/QZ0mS9UnWn7JqzSxGLUmSJEmSJM2+LhZ/GBzOOjWMdZuq6gU0lXVfAZ4+Q59lVbWgqhacsPiY2YpVkiRJkiRJI5bUSLe+6iIxdw5wbJIjgXlVdemOnFRVvwDOBH5/lMFJkiRJkiRJ47DnuG9YVZuTrAWWs51quXZeuQdU1dXt998G/nP0UUqSJEmSJGlc+jwP3CiNPTHXWgGczcAKrUkuoplDbr8kG4ETgE8BH0hyZyDAF4E/Gn+4kiRJkiRJ0uzqJDFXVStpEm2DbUfP0P3Ro49IkiRJkiRJXenzPHCj1MUcc5IkSZIkSdKc19VQVkmSJEmSJAmYu5VjJuYkSerI6qUbug5hK8eddFDXIWylb+9HkiTdfnWnfboOYYtNH/lo1yHcxoHHPrfrEHrDoaySJEmSJEmSxsaKOUmSJEmSJHUqWDEnSZIkSZIkaUysmJMkSZIkSVKnnGNOkiRJkiRJ0tiMNTGXZG2ShUNtS5OcnOT8JNcnWTXDuX+fZPN4IpUkSZIkSdK4hBrp1lfjrphbAUwOtU227W8HnjPdSUkWAHcdbWiSJEmSJEnS+Iw7MXcWsDjJ3gBJ5gMHABdX1YXADcMnJNmDJmn3yvGFKUmSJEmSpHFJaqRbX401MVdVm4B1wKK2aRI4s6q29YZeApxXVd/Z1rWTLEmyPsn6U1atmZ2AJUmSJEmSpBHpYvGHweGsU8NYp5XkAOB/A3+/vYtW1bKqWlBVC05YfMysBCpJkiRJkqTR68Mcc0kWJflqkquTvHqa4y9O8qUklyW5OMlhO/vcXSTmzgGOTXIkMK+qLt1G34cBBwNXJ7kW2DfJ1WOIUZIkSZIkSXNEO5Xae4AnAYcBz5gm8fbhqnpIVR0BvA14187ed8+dvcDtVVWbk6wFlrONarm278eBe03tJ9lcVQePNkJJkiRJkiSN00T3K6ceBVxdVdcAJDkDeApw5VSHqvrRQP9fgp0PuouKOWgScocDZ0w1JLkI+ChNNd3GJAs7ik2SJEmSJEm7kcG1CdptyVCX+wDfGtjf2LYNX+dPknydpmLuT3c2rrFXzAFU1UogQ21H78B5+40sKEmSJEmSJHVi1CunVtUyYNm2QpjutGmu8x7gPUmeCfwl8LydiaurijlJkiRJkiSpLzYC9x3YPxC4bhv9zwCeurM3NTEnSZIkSZKkTvVgVdbPA4ckuV+SvYBJ4LytYkwOGdh9MnDVzj53J0NZJUlS/6xeuqHrELZy3EkHdR3CVvr2fiRJ2hVM/OynXYewxf5PXdx1COqxqro5yUuAC4A9gOVV9eUkbwDWV9V5wEuSHAf8HPghOzmMFUzMSZIkSZIkqWM7WNU2UlX1CeATQ22vHfj+0tm+p0NZJUmSJEmSpA5YMSdJkiRJkqRO9aFirgtWzEmSJEmSJEkdsGJOkiRJkiRJnUqsmBu5JGuTLBxqW5rk5CTnJ7k+yaqh46cm+UaSy9rtiHHGLEmSJEmSJI3CuCvmVgCTNEvPTpkETgT2AvYFXjTNeSdW1VmjD0+SJEmSJEnj5hxz43EWsDjJ3gBJ5gMHABdX1YXADWOOR5IkSZIkSerEWBNzVbUJWAcsapsmgTOrantp0TcnuTzJ304l9YYlWZJkfZL1p6xaM4tRS5IkSZIkaZRCjXTrqy5WZZ0azkr7uWI7/V8DHAo8Arg78KrpOlXVsqpaUFULTlh8zGzFKkmSJEmSpBEzMTc+5wDHJjkSmFdVl26rc1V9pxo3Ae8HjhpHkJIkSZIkSdIojXvxB6pqc5K1wHK2Xy1HkntX1XeSBHgqcMWIQ5QkSZIkSdIY9bmqbZTGnphrrQDO5tYhrSS5iGbI6n5JNgInVNUFwOlJ7gEEuAx4cQfxSpIkSZIkSbOqk8RcVa2kSbQNth09Q98njCUoSZIkSZIkdSLc0nUInehijjlJkiRJkiRpzutqKKskSZIkSZIEQOIcc5IkSb2xeumGrkPYynEnHdR1CFv07d1IkrQruOk/vtB1CLexz7HP7ToEdczEnCRJkiRJkjqVmpsVc84xJ0mSJEmSJHXAijlJkiRJkiR1KlgxJ0mSJEmSJGlMrJiTJEmSJElSp1K3dB1CJ8ZaMZdkbZKFQ21Lk5yc5Pwk1ydZNXQ8Sd6c5GtJvpLkT8cZsyRJkiRJkjQK466YWwFMAhcMtE0CJwJ7AfsCLxo65/nAfYFDq+qWJL8yhjglSZIkSZI0Js4xNx5nAYuT7A2QZD5wAHBxVV0I3DDNOX8EvKGqqWmsqu+NJ1RJkiRJkiRpdMaamKuqTcA6YFHbNAmcWVXbSos+AHh6kvVJ/iXJIdN1SrKk7bP+lFVrZjdwSZIkSZIkjUzqlpFufdXFqqxTw1lpP1dsp//ewI1VtQD4J2D5dJ2qallVLaiqBScsPmbWgpUkSZIkSZJGoYvE3DnAsUmOBOZV1aXb6b8R+Fj7fSXw0FEGJ0mSJEmSpPEKNdKtr8aemKuqzcBamsq37VXLQZPIe0L7/XHA10YTmSRJkiRJkjQ+416VdcoK4GxuHdJKkouAQ4H9kmwETqiqC4C/AU5P8jJgM/DCDuKVJEmSJEnSiPR5HrhR6iQxV1UrgQy1HT1D3+uBJ48jLkmSJEmSJGlcuqqYkyRJkiRJkgB6PQ/cKHWx+IMkSfp/7N17mGRlee/97w90EIOoQTSMh5moeKFGUTLyJrpFEQzEkKiJyrgNOoghh23MmDdGfUOy82ZrgjEb2ZqQZJSDHByQw6ABIhFkAhoQBxw5iHIwoGRQIkp0EkGh7/1HrR6q2+6eA13rqaG/n+taV1c9tarqV6uqn+6++15rSZIkSVrw7JiTJEnaAhetvK11hE0OPHZJ6whTjNO2kSSNl9phx9YRNrn7xvH7efXo1gHGSMqOOUmSJEmSJEk9sWNOkiRJkiRJTXlWVkmSJEmSJKkBT/4gSZIkSZIkqTd2zEmSJEmSJKmphbora68dc0nWJjlo2tjKJMcl+VSSu5OcN+32y5Ks75YNSc7tM7MkSZIkSZI0Cn13zK0GlgMXDo0tB94BLAIeCfzm8B2q6sWTl5OcDXxi9DElSZIkSZLUl5THmOvDWcAhSXYCSLIUWAx8tqouBr4/2x2TPAp4GWDHnCRJkiRJkrZ7vRbmquou4Erg4G5oOXBG1RaVRV8NXFxV35vpxiRHJlmXZN3x510yP4ElSZIkSZI0cmFipMu4anFW1sndWem+rt7C+71+rnWralVVLauqZUccsv+DjChJkiRJkiSNVovC3LnAAUn2AXauqqs3d4ckuwH7AuePOpwkSZIkSZJ6VjXaZUz1Xpirqo3AWuAEtrxb7rXAeVV1z6hySZIkSZIkSX3q+6ysk1YD5/DALq0kuQzYC9glye3AEVU1efbW5cDRvaeUJEmSJEnSyKXG9zhwo9SkMFdVa4BMG3vxHOu/dNSZJEmSJEmSpD616piTJEmSJEmSAAjjexy4UWpx8gdJkiRJkiRpwbNjTpIkaTtz0crbWkeY4sBjl7SOMMW4bR9JWsjqYYtaR9jk8fvt2zqC5rBQjzFnx5wkSZIkSZLUgB1zkiRJkiRJaqs8xpwkSZIkSZKkntgxJ0mSJEmSpKY8xpwkSZIkSZKk3vRamEuyNslB08ZWJjkuyaeS3J3kvGm3H5Dk6iTrk3w2ydP7zCxJkiRJkqTRStVIl3HVd8fcamD5tLHl3fj7gcNmuM/fAm+oqucBHwOOGmlCSZIkSZIkqQd9H2PuLOA9SXaqqnuTLAUWA5+tqkry0hnuU8Cu3eVHAxv6CCpJkiRJkqSeeIy50auqu4ArgYO7oeXAGVVz9hS+Bbggye0MOuqOnmmlJEcmWZdk3fHnXTKfsSVJkiRJkqR51+LkD8O7s07uxjqXtwOvqKonAScCx8y0UlWtqqplVbXsiEP2n7ewkiRJkiRJGq3UxEiXcdWiMHcucECSfYCdq+rq2VZMsjuwd1V9vhs6A3hhDxklSZIkSZKkker7GHNU1cYka4ET2Hy33HeBRyd5RlXdCLwcuGHEESVJkiRJktSjML5nTh2l3gtzndXAOQydoTXJZcBewC7d8eSOqKoLk/wGcHaSCQaFuje3CCxJkiRJkiTNpyaFuapaA2Ta2IvnWHdNH7kkSZIkSZLUwBgfB26UWnXMSZIkSZIkSQCkFuaurC1O/iBJkiRJkiQteHbMSZIk6UG5aOVtrSNMceCxS1pH2GTcto0k9W2H73yrdYRNJpbs2TqC5rJAd2W1Y06SJEmSJElqwI45SZIkSZIkteUx5iRJkiRJkiT1xY45SZIkSZIkNRWPMSdJkiRJkiSpL70W5pKsTXLQtLGVSY5L8qkkdyc5b9rtL0tydZLrknw0iV1+kiRJkiRJDyVVo13GVN8dc6uB5dPGlnfj7wcOG74hyQ7AR4HlVfUzwG3Am3rIKUmSJEmSJI1U34W5s4BDkuwEkGQpsBj4bFVdDHx/2vq7AfdW1Y3d9U8Dv9ZPVEmSJEmSJPUhNTHSZVz1WpirqruAK4GDu6HlwBlVs/YUfht4eJJl3fXXAE+eacUkRyZZl2Td8eddMp+xJUmSJEmSpHnX4nhtk7uzfqL7+ubZVqyqSrIc+EDXZfdPwH2zrLsKWAVwz8Unj+/Ow5IkSZIkSZpqjLvaRqlFYe5c4Jgk+wA7V9XVc61cVZcDLwZI8gvAM0YfUZIkSZIkSRqt3gtzVbUxyVrgBAbdc3NK8viqurPrmHsn8N4RR5QkSZIkSVKPMsZnTh2lvk/+MGk1sDdw+uRAksuAM4EDktye5KDupnckuQG4BviHqvpM72klSZIkSZKkedZiV1aqag2QaWMvnmXddwDv6COXJEmSJEmSGphYmMeYa9UxJ0mSJEmSJC1oTTrmJEmSJEmSpE0W6DHmLMxJkiTpIeWilbe1jrDJgccuaR1hinHaNpIWhmv3+vXWETZ59jc/1TqC9GMszEmSJEmSJKmt8hhzkiRJkiRJknpix5wkSZIkSZKaygI9xpwdc5IkSZIkSVIDFuYkSZIkSZLUVk2MdtkCSQ5O8tUkNyd51wy375TkjO72zydZ+mBfdq+FuSRrkxw0bWxlkguSXJ7k+iTXJDl06Paf7l7sTd2LX9RnZkmSJEmSJD20JdkR+BvgF4FnAa9P8qxpqx0BfLeqng58AHjfg33evjvmVgPLp40tZ/BC3lhVzwYOBo5N8pju9vcBH6iqPYHvMtgIkiRJkiRJeqho3zG3L3BzVX2tqn4InA68cto6rwQ+2l0+CzggSR7My+67MHcWcEiSnQC6lr/FwKVVdRNAVW0A7gR2717cy7r7weDFv6rnzJIkSZIkSRqhVI12SY5Msm5oOXJahCcC3xi6fns3NuM6VXUf8B/Abg/mdfdamKuqu4ArGXTFwaBb7oyqB069kWRfYBFwC4MXd3f3YmHmjTJ5v00b+PjzLhnVS5AkSZIkSdJ2pqpWVdWyoWXVtFVm6nybfqrYLVlnqzzswdx5G03uzvqJ7uubJ29IsgdwCvCmqpqYpR1wxhfcbdBVAPdcfPLCPMeuJEmSJEnS9mhiy07QMEK3A08euv4kYMMs69ye5GHAo4HvPJgnbXFW1nMZ7IO7D7BzVV0NkGRX4HzgqKq6olv328BjuhcLM28USZIkSZIk6cH4ArBndxLSRQyayT45bZ1PAm/qLr8G+MzwXqDbovfCXFVtBNYCJzDonqN7wWuAk6vqzKF1C7iEwYuFwYv/RJ95JUmSJEmSNGJVo102+/R1H/BW4ELgBuDjVXV9kj9L8ivdascDuyW5Gfh94F0P9mW32JUVBgW5c3jgDK2vA/Zj8OJWdGMrqmo98E7g9CTvAb7IYCNIkiRJkiRJ86aqLgAumDb2J0OX7wFeO5/P2aQwV1VrGDpgXlWdCpw6y7pfY3DKWkmSJEmSJD0UVfNjzDXR4hhzkiRJkiRJ0oLXaldWSZIkSZIkaeDBnUNhu2VhTpIkSRqRi1be1jrCFAceu6R1hCnGbftImn97/tfVrSNs8sNdH8+i//pu6xjSFBbmJEmSJEnSQ55FuTE34THmJEmSJEmSJPXEjjlJkiRJkiS15VlZJUmSJEmSJPXFjjlJkiRJkiS1tUDPytprx1yStUkOmja2MskFSS5Pcn2Sa5IcOnT7W5PcnKSSPK7PvJIkSZIkSdKo9N0xtxpYDlw4NLYceCewoapuSrIYuCrJhVV1N/A54Dxgbc9ZJUmSJEmS1AfPytqLs4BDkuwEkGQpsBi4tKpuAqiqDcCdwO7d9S9W1a0955QkSZIkSZJGqtfCXFXdBVwJHNwNLQfOqHpgR+Ik+wKLgFu25rGTHJlkXZJ1x593yXxFliRJkiRJ0qhVjXYZUy3Oyjq5Oyvd19WTNyTZAzgFOLxq686TW1WrqmpZVS074pD95y2sJEmSJEmSNAotzsp6LnBMkn2AnavqaoAkuwLnA0dV1RUNckmSJEmSJKmFrevPesjovWOuqjYyOJHDCXTdckkWAWuAk6vqzL4zSZIkSZIkSX1rsSsrDApyewOnd9dfB+wHrEiyvlueB5DkbUluB54EXJPkI00SS5IkSZIkaTQmarTLmGqxKytVtQbI0PVTgVNnWfeDwAd7iiZJkiRJkiT1oklhTpIkSZIkSdrEY8xJkiRJkiRJ6osdc5IkSdICcdHK21pHmOLAY5e0jjDFuG0f6aHgEbdd1zrCVI96TOsEms2EHXOSJEmStCBZlJMWAItyGkN2zEmSJEmSJKmtGt8zp46ShTlJkiRJkiS15ckfJEmSJEmSJPXFjjlJkiRJkiS1NbEwd2XttWMuydokB00bW5nkgiSXJ7k+yTVJDh26/bQkX01yXZITkjy8z8ySJEmSJEnSKPS9K+tqYPm0seXA+4A3VtWzgYOBY5NMni7lNGAv4DnAzsBbesoqSZIkSZKkHlRNjHQZV30X5s4CDkmyE0CSpcBi4NKqugmgqjYAdwK7d9cvqA5wJfCknjNLkiRJkiRJ867XwlxV3cWguHZwN7QcOKMrugGQZF9gEXDL8H27XVgPAz4102MnOTLJuiTrjj/vklHElyRJkiRJ0ihM1GiXMdXirKzDu7Mu764DkGQP4BTg8PrxPsPjGHTWXTbTg1bVqqpaVlXLjjhk/xHEliRJkiRJkuZPi7Oyngsck2QfYOequhogya7A+cBRVXXF8B2S/E8Gns+l3wAAIABJREFUu7b+Zt9hJUmSJEmSNGJjfBy4Ueq9MFdVG5OsBU6g65ZLsghYA5xcVWcOr5/kLcBBwAEzdNFJkiRJkiRJ26UWHXMwKMidwwO7tL4O2A/YLcmKbmxFVa0H/g64Dbg8CcA5VfVn/caVJEmSJEnSqNTEwuzFalKYq6o1QIaunwqcOsu6rYqHkiRJkiRJ0shY9JIkSZIkSVJbNb5nTh2lFmdllSRJkiRJkhY8O+YkSZIkNXHRyttaR5jiwGOXtI6wybhtG2lb1caNrSM8YONGsseTWqfQbBboMebsmJMkSZK04I1TUU7SaFiU0ziyY06SJEmSJElteYw5SZIkSZIkSX2xY06SJEmSJElNlceYkyRJkiRJktSXXgtzSdYmOWja2MokFyS5PMn1Sa5JcujQ7ccn+VI3flaSXfrMLEmSJEmSpBGbqNEuY6rvjrnVwPJpY8uB9wFvrKpnAwcDxyZ5THf726tq76p6LvB14K29pZUkSZIkSZJGpO9jzJ0FvCfJTlV1b5KlwGLg0qrB6TeqakOSO4Hdgbur6nsASQLsDIxvmVOSJEmSJElbrcpjzI1cVd0FXMmgKw4G3XJnTBblAJLsCywCbhkaOxH4JrAX8KGZHjvJkUnWJVl3/HmXjOgVSJIkSZIkSfOjxckfhndnXd5dByDJHsApwOE1VCqtqsMZdNbdABzKDKpqVVUtq6plRxyy/6iyS5IkSZIkab55jLnenAsckGQfYOequhogya7A+cBRVXXF9DtV1f3AGcCv9RlWkiRJkiRJGoW+jzFHVW1MshY4ga5bLskiYA1wclWdOblud1y5p1XVzd3lXwa+0ndmSZIkSZIkjdACPcZc74W5zmrgHB7YpfV1wH7AbklWdGMrgGuAj3bddAG+BPx2r0klSZIkSZI0UjXGu5uOUpPCXFWtYVBom7x+KnDqLKu/qJdQkiRJkiRJUo9adcxJkiRJkiRJAxMLc1fWFid/kCRJkiRJkhY8O+YkSZIkSZLUVJXHmJMkSZKkBemilbe1jjDFgccuaR1hinHbPtqOLH5y6wQPuP/+1gmkH2NhTpIkSZIkSW15jDlJkiRJkiRJfbFjTpIkSZIkSU3VxMI8xpwdc5IkSZIkSVIDvRbmkqxNctC0sZVJLkhyeZLrk1yT5NAZ7vuhJBv7SytJkiRJkqRe1MRolzHV966sq4HlwIVDY8uBdwIbquqmJIuBq5JcWFV3AyRZBjym56ySJEmSJEnSyPS9K+tZwCFJdgJIshRYDFxaVTcBVNUG4E5g926dHYH3A3/Yc1ZJkiRJkiT1oCZqpMu46rUwV1V3AVcCB3dDy4EzqmrTFkqyL7AIuKUbeivwyaq6o8+skiRJkiRJ0ii1OPnD5O6sdF9XT96QZA/gFODwqprodmt9LfChzT1okiOTrEuy7vjzLhlBbEmSJEmSJI1CTUyMdBlXfR9jDuBc4Jgk+wA7V9XVAEl2Bc4HjqqqK7p1nw88Hbg5CcAjk9xcVU+f/qBVtQpYBXDPxSePb4+iJEmSJEmSRIPCXFVtTLIWOIGuWy7JImANcHJVnTm07vnAT01eT7JxpqKcJEmSJEmStmNjfBy4UWqxKysMCnJ7A6d3118H7AesSLK+W57XKJskSZIkSZI0ci12ZaWq1gAZun4qcOoW3G+XUeaSJEmSJElS/6rG9zhwo9SqY06SJEmSJEla0Jp0zEmSJEmSJEmTymPMSZIkSZIkSeqLHXOSJEmSNGYuWnlb6whTHHjsktYRphi37aPZZeP3WkfYpHbZtXUEzWXCY8xJkiRJkiRJ6okdc5IkSZIkSWrKY8xJkiRJkiRJ6o0dc5IkSZIkSWqqPMbc6CVZm+SgaWMrk1yQ5PIk1ye5JsmhQ7eflORfk6zvluf1mVmSJEmSJEmjVVUjXcZV3x1zq4HlwIVDY8uBdwIbquqmJIuBq5JcWFV3d+u8o6rO6jmrJEmSJEmSNDJ9F+bOAt6TZKequjfJUmAxcGl15cuq2pDkTmB34O5ZH0mSJEmSJEkPDe7KOnpVdRdwJXBwN7QcOKOGegqT7AssAm4Zuut7u11cP5Bkp94CS5IkSZIkSSPS4qysk7uz0n1dPXlDkj2AU4DDq2qyVPpuYC/gBcBPMtjt9cckOTLJuiTrjj/vklFllyRJkiRJ0jyriRrpMq5aFObOBQ5Isg+wc1VdDZBkV+B84KiqumJy5aq6owbuBU4E9p3pQatqVVUtq6plRxyy/+hfhSRJkiRJkvQg9H2MOapqY5K1wAl03XJJFgFrgJOr6szh9ZPsUVV3JAnwKuC6niNLkiRJkiRphMa5q22Uei/MdVYD5/DALq2vA/YDdkuyohtbUVXrgdOS7A4EWA/8Vs9ZJUmSJEmSpHnXpDBXVWsYFNomr58KnDrLui/rK5ckSZIkSZL6V56VVZIkSZIkSVJfWu3KKkmSJEmSJAEL9xhzdsxJkiRJkiRJs0jyk0k+neSm7utjZ1hnSZKrkqxPcn2SLTpHgh1zkiRJkqQ5XbTyttYRpjjw2CWtI2wybttm7PzEo1on0HZizI8x9y7g4qo6Osm7uuvvnLbOHcALq+reJLsA1yX5ZFVtmOuB7ZiTJEmSJEmSZvdK4KPd5Y8Cr5q+QlX9sKru7a7uxBbW3OyYkyRJkiRJUlOjPsZckiOBI4eGVlXVqi28+xOq6g6AqrojyeNneY4nA+cDTwfesbluObAwJ0mSJEmSpIe4rgg3ayEuyUXAT81w0x9txXN8A3huksXAuUnOqqpvzXUfC3OSJEmSJElqq9qelbWqDpzttiTfSrJH1y23B3DnZh5rQ5LrgRcDZ821bq/HmEuyNslB08ZWJrkgyeXdWSuuSXLo0O1J8t4kNya5Icnb+swsSZIkSZKkBe2TwJu6y28CPjF9hSRPSrJzd/mxwIuAr27ugfvumFsNLAcuHBpbzuBMFhuq6qau3e+qJBdW1d3ACuDJwF5VNTHbfrySJEmSJEnaPo35WVmPBj6e5Ajg68BrAZIsA36rqt4CPBP430kKCPBXVXXt5h6478LcWcB7kuzUnT52KbAYuLRq0LPYtfvdCewO3A38NvDfq2qiu33OdkFJkiRJkiRpvlTVXcABM4yvA97SXf408Nytfexed2XtXsiVwMHd0HLgjMmiHECSfYFFwC3d0NOAQ5OsS/KPSfbsM7MkSZIkSZJGqyZqpMu46rUw15ncnZXu6+rJG7oD6J0CHD7ZIQfsBNxTVcuADwMnzPSgSY7sinfrjj/vkpGFlyRJkiRJkuZDi7Oyngsck2QfYOequhogya7A+cBRVXXF0Pq3A2d3l9cAJ870oMOnvb3n4pPHtxQqSZIkSZKkKcb8GHMj03vHXFVtBNYy6HxbDZBkEYOi28lVdea0u5wLvKy7/BLgxn6SSpIkSZIkSaPTomMOBgW5c3hgl9bXAfsBuyVZ0Y2tqKr1DM58cVqStwMb6Q6qJ0mSJEmSpIeGcT4O3Cg1KcxV1RoGp46dvH4qcOos694N/FJP0SRJkiRJkqRetOqYkyRJkiRJkoCF2zHX4qyskiRJkiRJ0oJnx5wkSZIkSZKaWqhnZbUwJ0mSJEnarly08rbWETY58NglrSNMMU7bBmDi4Y9oHWGTTNzfOoLm4K6skiRJkiRJknpjx5wkSZIkSZKamrjfjjlJkiRJkiRJPbFjTpIkSZIkSU0t1JM/9Noxl2RtkoOmja1MckGSy5Ncn+SaJIcO3X5ZkvXdsiHJuX1mliRJkiRJkkah74651cBy4MKhseXAO4ENVXVTksXAVUkurKq7q+rFkysmORv4RK+JJUmSJEmSNFKelbUfZwGHJNkJIMlSYDFwaVXdBFBVG4A7gd2H75jkUcDLADvmJEmSJEmStN3rtWOuqu5KciVwMIPOt+XAGVW1qSyaZF9gEXDLtLu/Gri4qr7XV15JkiRJkiSNnh1z/ZncnZXu6+rJG5LsAZwCHF5V04/69/rhdadLcmSSdUnWHX/eJfMcWZIkSZIkSZpfLc7Kei5wTJJ9gJ2r6mqAJLsC5wNHVdUVw3dIshuwL4OuuRlV1SpgFcA9F5+8MMuskiRJkiRJ2yE75npSVRuBtcAJdB1wSRYBa4CTq+rMGe72WuC8qrqnr5ySJEmSJEnSKLXomINBQe4cHtil9XXAfsBuSVZ0Yyuqan13eTlwdK8JJUmSJEmS1IuamH5Es4WhSWGuqtYAGbp+KnDqHOu/tIdYkiRJkiRJUm9adcxJkiRJkiRJgMeYkyRJkiRJktQjO+YkSZIkSZLU1MT9C7NjzsKcJEmSJEnb6KKVt7WOMMWBxy5pHWGKT//+7a0jSGPNwpwkSZIkSZKa8hhzkiRJkiRJknpjx5wkSZIkSZKaqomJ1hGasGNOkiRJkiRJasCOOUmSJEmSJDXlMeZ6kGRtkoOmja1MckGSy5Ncn+SaJIcO3X5AkquTrE/y2SRP7zOzJEmSJEmSNAp9d8ytBpYDFw6NLQfeCWyoqpuSLAauSnJhVd0N/C3wyqq6IcnvAEcBK3rOLUmSJEmSpBGZuN+OuT6cBRySZCeAJEuBxcClVXUTQFVtAO4Edu/uU8Cu3eVHAxt6zCtJkiRJkiSNRK+Fuaq6C7gSOLgbWg6cUVWbyqJJ9gUWAbd0Q28BLkhyO3AYcPRMj53kyCTrkqw7/rxLRvUSJEmSJEmSNM9qoka6jKsWZ2Wd3J2V7uvqyRuS7AGcAhxeVZPnyX078IqqehJwInDMTA9aVauqallVLTvikP1HFl6SJEmSJEnzqyYmRrqMqxaFuXOBA5LsA+xcVVcDJNkVOB84qqqu6MZ2B/auqs939z0DeGGDzJIkSZIkSdK86vvkD1TVxiRrgRPouuWSLALWACdX1ZlDq38XeHSSZ1TVjcDLgRt6jixJkiRJkqQRqgV68ofeC3Od1cA5PLBL6+uA/YDdkqzoxlZU1fokvwGcnWSCQaHuzX2HlSRJkiRJkuZbk8JcVa0BMnT9VODUOdZd01M0SZIkSZIk9WxigXbMtTjGnCRJkiRJkrTgtdqVVZIkSZIkSQKgJhZmx9xDsjD3X5d/rnWEKS7//9e2jrDJi97z8tYRpsjLX9U6whT/9r/+onWETX7i/ce1jjDF137wlNYRpvi5a49tHWGK7PqY1hE2qe/d3TrCFHnCE1tHmGqMTpVeO+7YOsIU9fBHtI4wxQ4//EHrCGOrdhizz87DFrWOMMUO3/lW6wibXLvXr7eOMMWe/3V16whTPOK261pH2KQ2bmwdYarFT26dYIps/F7rCFP9xKNaJ9hkYsx+fn76929vHWGKlx/zpNYRNvn027/eOoL0Yx6ShTlJkiRJkiRtPzzGnCRJkiRJkqTe2DEnSZIkSZKkpur+8TnkTJ/smJMkSZIkSZIasGNOkiRJkiRJTS3Us7JuUcdcklcnqSR7jTrQHBlWJnlkq+eXJEmSJEmS5tOW7sr6euCzwPIRZtmclYCFOUmSJEmSpIeYiftrpMu42mxhLskuwIuAI+gKc0lemuSfk3w8yY1Jjk7yhiRXJrk2ydO69ZYkuTjJNd3Xp3TjJyV5zdBzbBx63LVJzkrylSSnZeBtwGLgkiSXzPtWkCRJkiRJknq2JR1zrwI+VVU3At9Jsk83vjfwe8BzgMOAZ1TVvsBHgN/t1vlr4OSqei5wGvDBLXi+5zPojnsW8FTgRVX1QWADsH9V7T/TnZIcmWRdknUf/cINW/A0kiRJkiRJGgd1f410GVdbUph7PXB6d/n07jrAF6rqjqq6F7gF+Kdu/FpgaXf554GPdZdPAf7bFjzflVV1e1VNAOuHHmtOVbWqqpZV1bI3veCZW3IXSZIkSZIkqZk5z8qaZDfgZcDPJClgR6CAC4B7h1adGLo+McfjTpYo76MrCiYJsGhoneHHvX9zGSVJkiRJkrR9m7hvfLvaRmlzHXOvYbAr6pKqWlpVTwb+lS3rfAP4Fx44YcQbGJxAAuBW4Ge7y68EHr4Fj/V94FFb+LySJEmSJEnSWNtcN9rrgaOnjZ0N/DaD3Vc3523ACUneAfw7cHg3/mHgE0muBC4G/nMLHmsV8I9J7pjtOHOSJEmSJEna/tSPFmbH3JyFuap66QxjH2TaSRyG16uqtcDa7vKtDHaFnf4Y3wJ+bmjo3dPv211/69DlDwEfmiuvJEmSJEmStL3w+G2SJEmSJElqymPMSZIkSZIkSepNqh56FckffOwvxupF/WivZa0jPGDt+a0TTPGjX3hd6whT/MS1l7WO8IBH7do6wRQ/vPmm1hGmeNjz920dYYodNtzWOsImP7h1fLIA3PfKFa0jTFGnHdc6wia7vuzHjvbQ1LfPPa91hCke96pDWkeY4t4vXtU6wiZ33zhe3+eP32+85uRasmfrCA+YuL91gqketiXnXOvPDvdsyaGmF6iJidYJpqgdd2wdYXztMGY7otV4fXbGKc/LP/CU1hF+zGf/4SVpnWFcfPoJPzPSWs7Lv3XdWG5rO+YkSZIkSZKkBsastC9JkiRJkqSFZqEeY87CnCRJkiRJkpqqH43Pbs99cldWSZIkSZIkqQE75iRJkiRJktTUQt2VdSQdc0l+KsnpSW5J8uUkFyR5RpLrRvF8kiRJkiRJ0vZm3jvmkgRYA3y0qpZ3Y88DnjDfzyVJkiRJkqTtX/3Ijrn5sj/wo6r6u8mBqloPfGPyepKlSS5LcnW3vLAb3yPJpUnWJ7kuyYuT7JjkpO76tUnePoLMkiRJkiRJUr+qal4X4G3AB2YYXwpc111+JPCI7vKewLru8v8L/FF3eUfgUcDPAp8eepzHzPK8RwLruuXIeXot8/I487htzbMdZDHP9pPFPNtXnnHKYp7tJ4t5tq8845TFPNtPFvNsX3nGKYt5tp8s5nF5KC+tzsr6cODDSa4FzgSe1Y1/ATg8yZ8Cz6mq7wNfA56a5ENJDga+N9MDVtWqqlrWLavmKeeR8/Q488U8sxunLGCeuYxTFjDP5oxTnnHKAuaZyzhlAfNszjjlGacsYJ65jFMWMM/mjFOeccoC5pnLOGUB8+ghahSFuesZdLnN5e3At4C9gWXAIoCquhTYD/g34JQkb6yq73brrQX+B/CREWSWJEmSJEmSejWKwtxngJ2S/MbkQJIXAEuG1nk0cEdVTQCHMdhtlSRLgDur6sPA8cA+SR4H7FBVZwN/DOwzgsySJEmSJElSr+b9rKxVVUleDRyb5F3APcCtwMqh1Y4Dzk7yWuAS4D+78ZcC70jyI2Aj8EbgicCJSSaLiO+e78xzmK9dYueLeWY3TlnAPHMZpyxgns0ZpzzjlAXMM5dxygLm2ZxxyjNOWcA8cxmnLGCezRmnPOOUBcwzl3HKAubRQ1SqFubpaCVJkiRJkqSWWp38QZIkSZIkSVrQLMxJkiRJkiRJDViYA5KsTXLQtLGVSY5L8qkkdyc5r3GWC5JcnuT6JNckObRxnhOTXJVkfZfptxrnOa67vGuSf0vy1y2zJLm/2zbrk3xy1Fm2IM9TkvxTkhuSfDnJ0oZ5bhjaNuuT3JPkVQ3zHJfkL7vP8Q1JPpgkDbO8L8l13TKy7/NtmfeS/HSSzye5KckZSRY1zvPWJDcnqe5EQS2znJbkq937dkKShzfOc3ySL3U/L85KskvLPEPrfSjJxpZZkpyU5F+H5qDnNc6TJO9NcmM3B72tcZ7LhrbNhiTnNs5zQJKruzyfTfL0hlle1mW5LslHk8zbsZrnyDPr73+N5uS58vQ9J8+VpcWcPFeeFnPyZv92mO85eVvzjGpe3sYsLebkufK0mJPnyrPNc3KSV3fzw17z9Rq2Vvf6HjnH7T+V5PQkt2TwN9MFSZ6R5Lo+c2oBqqoFvwC/CZw4bewK4MXAAcAvA+c1zvISYM/u+mLgDuAxjfPs1F3fhcEJPha3fK+6y/8H+Bjw140/Nxv7+LxsRZ61wMuH3q9Htn6vuus/CXyncZ6XAJ9jcHboHYHLgZc2yvI/gU8zODHPTwDrgF0bfF5mnPeAjwPLu8t/B/x24zzPB5Z288/jGmd5BZBuWT0G22bXocvHAO9qmadbZxlwynzOj9u4bU4CXjNfGeYhz+HAyQzOQA/w+Nbv1dC6ZwNvbLx9bgSe2V3+HeCkFlkY/DP7G8Azuut/BhzRw7aZ9fc/2szJc+Xpe06eK0uLOXmuPC3m5Dn/dmAEc/KD2D4nMYJ5eRuztJiTt+jvPPqbk+faPts8JzOYsy4D/nS+3+utyDDr/NTNF5cDvzU09jwGPxeua5XZZWEszQOMwwLsBvw7DxSalgJf54GTY7yU/gpzc2YZWu9LkxNm6zzdOl+nn8LcrHmAnwVOB1bQT2FuriwtCnOz5Xk28NkxyjP82TkSOK1xnp8HrgJ2Bh7JoBj2zEZZ3gEcNbTe8cDrWrw/0+e97nP9beBh3fWfBy5slWfafW9lfv8IfFA/E4C3A+8dhzzd+/a3wDtb5mFQ9L4E2IP5LcxtS5aTGF1hblvyXAk8fVzyDN33UcB3mcd/Dmzj9vkq8P90l98N/HmLLMDuwM1D118MXNDXthla70vAnjSek6fnmTZ2Kz3OyXNl6cZ7nZM3s216n5NnysOI5uQHkeckRlOY25YszebkzXx2ep+TZ9k+2zQnM2gM+DfgGcBXurGXAv/MoGB3I3A08IbuPbgWeFq33hLgYuCa7utTZvrcTH6Wu8ddC5wFfAU4rfveexvww+6xL5kh48uAS2cYX0pXmOsuXwZc3S0v7Mb3AC4F1gPXMfgZsWOX8bruOd8+is+Vy0NjcVdWoKruYjABHNwNLQfOqKoaxyxJ9gUWAbe0zJPkyUmuYfAf5PdV1YZWeRhMtv+bQWGjF5t5rx6RZF2SK9LDbppz5WHwC/zdSc5J8sUk70+yY6s8076vljP4T/bIzZHncga/nN7RLRdW1Q0tsjD4xecXkzyy2w1of+DJfWaYY97bDbi7qu7rrt8OPLFhnpF5MFm63aUOAz7VOk+SE4FvAnsBH2qc563AJ6vqjvnK8SCyALy32z3nA0l2apznacCh3c+Mf0yyZ+M8k14NXFxV32uc5y3ABUluZ/C9dXSjLN8GHp5kWXf9Nczj/LwNv/81n5P7+n30wWRpNSfPlKflnDxDnpHMyQ8iD4xgXt7GLE3n5Dm+r5rMyTPk2dY5+VXAp6rqRuA7SfbpxvcGfg94Tvd4z6iqfYGPAL/brfPXwMlV9VwGRbYPbsHzPR9YCTwLeCrwoqr6ILAB2L+q9p/hPj/D4J/1c7mTwV5I+wCHDmX57wz+hnhe95rWM+i2e2JV/UxVPQc4cQtya4GyMPeA1QwmI+ixWLC1WZLswaDt/PCqmmiZp6q+0U2QTwfelOQJDfP8DoP/XH+jpwxzZYHBf3KWMZikj03ytIZ5HsbgvzZ/ALyAwQ+nFQ3zAJs+y88BLuwpy4x5umNjPBN4EoM/al6WZL8WWarqn4ALgH/pbr8cuG/mu48mwxzrznTcvfkumm0X8/BmHMfgv62Xtc5TVYcz2AXlBga/PDbJk2Qx8Frm8Q/Rbc3SeTeDP4xfwGB3+nc2zrMTcE/3M+PDwAmN80x6/VasO8o8bwdeUVVPYvBHzTEtsnR/qC4HPpDkSuD7zP/8vDW//zWdkxv8PrqtWXqfk2fL02pOnp6nhzl5q/J0w6Ocl7c2S7M5eTOf5d7n5FnybOuc/HoGezbRfX19d/kLVXVHVd3LoPj3T934tQy602DQEfyx7vIpwH/bgue7sqpu73KvH3qsB+vhwIeTXAucyaDwB/AF4PAkfwo8p6q+D3wNeGoGx3I8GJi3oqoegqa30C3UhUF77Z3APsBXp932UnralXWuLMCuDFpmXzsu22ZonRMZ0a5BW5KHwX9Pvs5g94lvM5j4jh6TbXNS423zc8DaoXUOA/6m9WeHwX/HVvWRYzPb5x3AHw+t8yfAH47JZ+djDH75afH+TJn3GPFuU1ubZ9pttzKPu01taxYGxwg8l+64NK3zDN3+krlu7+Gz80sMukRu7ZYJhnYJbLxt5ry9jzwMdrNZ2l0O8B+tPzsMurHuAh4xn1m24bOzO3DL0PWnAF8ek8/OLwAf72PbMMPvfzSck2fKM+1+t9LTnDxXFhrMyZvbNt06vc3Js3x2Rjonz8P2mfN7b9RZaDQnb+az3PucPMtnZ5vm5C7/D4Dbus/cNxj83bY/U+f8tcCy6Z8Duo7l7vLDgX/vLn+E7pAv3Xv1w5k+Qww67lZ0l29l9mPMHcDmd2X9U+CvGDQ4PQy4b2i9xcBvMCgqvnFo+/4a8A/ACfP9/rk8dBY75jpVtZHBZHACbbs0ZsySwZm21jBo4z1zDPI8KcnO3eXHAi9icMyBJnmq6g1V9ZSqWsqgM+zkqnpXiyxJHjvZgt/tjvgi4MujzjJbHgb/wXlskt276y9rnGfSqP7zt7V5vg68JMnDul1eXsLgv9m9Z0myY5LdusvPBZ7LA/857CXDHOsWg11+X9MNvQn4RKs8o7a1WZK8BTgIeH2NoHtka/J0Z5R7+uRlBgex/0qrPFV1flX9VFUt7ebo/6qqeTmz5tZmgU0dAJPb5lUMjv0yb7bhc3wug3kZBvPPjY3zwKCb5ryqumc+s2xDnu8Cj07yjO76y5nH+XkbPjuP777uxKCj5+/mK8tseWb7/a/VnNzq99GtzdJiTp4tT6s5eY7Pzkjn5K3N0902snl5Gz7Hvc/JW/B91eucPEeebZ2TX9M91pLuc/dk4F/Zss43GOxJMtnV9wbgs93lWxkcYxzglQyKdpvzfQbH65vJZ4CdkvzG5ECSFzA4xt2kRwN3dPPKYQyOI0eSJcCdVfVhBseI3qf7O3CHqjob+GMGxU9pZq0rg+O0MNh3v4C9hsYuY3BQzB8wOH7HQS2yAL8O/IhBK+7k8rxW24bBRHwNg2NiXQMc2fq9GrptBT2c/GGObfNCBv8p+VL3dd7O2rat22bo/borPVLYAAAgAElEQVSWQQffosZ5ljI4AOy8/xd7G96vHYG/Z/CLxZeBYxpmeUSX4csMzoo18u/xrZn3GOwGfSVwM4P2/Z0a53lbd/0+BscM+UjDLPcx2AVjcn7+k1bbhsF/cT/Xfb9fx6CjeN7P7rs122fa/eb95Dhb+V59ZmjbnArs0jjPY4Dzu0yXA3u3fq8Y/JF28Hzn2Mbt82oe+Jm6FnhqwyzvZ/Cz4qvAyj62DXP8/keDOXkzeXqdkzeTpfc5ebY8NJqT59o+0+43khOWbeX7NdJ5eSuz9D4nb+69ouc5eTPbZ6vn5Jnyd/PFDWxZx9zS7jMy/eQPT2Dw+/KVwF8w9eQPs3XM/S6Dwvgls2RdzOBkFLcA13efhT15oGNuzy7HFdOe803d5/eLDH6G/DSDY81dPbQNf3FU76HL9r9MnnlKkiRJkiRJUo/clVWSJEmSJElqwMKcJEmSJEmS1ICFOUmSJEmSJKkBC3OSJEmSJElSAxbmJEmSJEmSpAYszEmSJEmSJEkNWJiTJEmSJEmSGrAwJ0mSJEmSJDVgYU6SJEmSJElqwMKcJEmSJEmS1ICFOUmSJEmSJKkBC3OSJEmSJElSAxbmJEmSJEmSpAYszEmSJEmSJEkNWJiTJEmSJEmSGrAwJ0mSJEmSJDVgYU6SJEmSJElqwMKcJEmSJEmS1ICFOUmSJEmSJKkBC3OSJEmSJElSAxbmJEmSJEmSpAYszEmSJEmSJEkNWJiTJEmSJEmSGrAwJ0mSJEmSJDVgYU6SJEmSJElqwMKcJEmSJEmS1ICFOUmSJEmSJKkBC3OSJEmSJElSAxbmJEmSJEmSpAYszEmSJEmSJEkNWJiTJEmSJEmSGrAwJ0mSJEmSJDVgYU6SJEmSJElqwMKcJEmSJEmS1ICFOUmSJEmSJKkBC3OSJEmSJElSAxbmJEmSJEmSpAYszEmSJEmSJEkNWJiTJEmSJEmSGrAwJ0mSJEmSJDVgYU6SJEmSJElqwMKcJEmSJEmS1ICFOUmSJEmSJKkBC3OSJEmSJElSAxbmJEmSJEmSpAYszEmSJEmSJEkNWJiTJEmSJEmSGrAwJ0mSJEmSJDVgYU6SJEmSJElqwMKcJEkPEUluTfIHrXM8GA+F1zCbJCuSbJyHx3lpkkryuPnINa7ma3tJkiSNMwtzkiQ1lmT3JMd1Ral7k3wrycVJXj6C59o7ySeSfDPJPUm+nuTsJEvm+7laSvK0JMcn+Ua3TW9NclaSF7bOtjVmKVT+C7AHcNeIn3tFVwC8aYbbXtHdtlWFsyRrk/z1Fq5+BvDUrXl8SZKk7Y2FOUmS2jsb2Bc4AngGcAjwj8Bu8/kkSXYHLgY2Ar8E7AUcBtwC7Dqfz9VSkmXA1cCzgd8BngX8CnAV8KEH8biLtmZ8VKrqh1X1zaqqHp7uHuAxSV4ybfzNwNdH9aRJHl5VP6iqO0f1HJIkSePAwpwkSQ0leQzwYuBdVXVxVd1WVV+oqr+qqtOH1vuxzqlZuo92SXJqko1dV9zwfV4EPBY4vKquqqpbq+qfq+oPq+raocc9OslXk/yge96/TPKIodv/NMl1Sd7U3b4xyYlJFiX5na5L7a4kxyTZYeh+t3b3nS3fTNvn0UlWJbkzyfeT/HNXeJtt/QAnAV8DXlRV/1BVt1TVNVX1F8ABQ+s+J8lF3ev8TpKTkjx66PaTkpyX5J1Jbgdun/Y6TkhyN3BaN/7EJKcn+W63nJ9kzzmyPm2oe/E/k1yd5JCh29cCS4D3d91p1Y3/2K6sSX41ybVdd+A3kvxRty2Gt/1RSf4+yfeS3J7kHXNt+879wCkMCnGTj/U4BsXjk6e9nt2SrO4e+wdJrk9y+PD2BF4C/I/J15Nk6dDreUWSK5P8EDgoQ7uyZuDT3fuVbmyXJDdtRQeeJEnS2LEwJ0lSWxu75VeGi18Pwu8DNwD7AP8T+PMkv9rd9k0GP/tfM1y0mcF/MijEPJNBx9ly4I+mrbMUeCWDAs2vAa8FPgG8APg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\n",
      "text/plain": [
       "<Figure size 1728x1440 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make sure we use the subsample in our correlation\n",
    "\n",
    "f, (ax1, ax2) = plt.subplots(2, 1, figsize=(24,20))\n",
    "\n",
    "# Entire DataFrame\n",
    "corr = data.corr()\n",
    "sns.heatmap(corr, cmap='coolwarm_r', annot_kws={'size':20}, ax=ax1)\n",
    "ax1.set_title(\"Imbalanced Correlation Matrix \\n (don't use for reference)\", fontsize=14)\n",
    "\n",
    "\n",
    "sub_sample_corr = new_df.corr()\n",
    "sns.heatmap(sub_sample_corr, cmap='coolwarm_r', annot_kws={'size':20}, ax=ax2)\n",
    "ax2.set_title('SubSample Correlation Matrix \\n (use for reference)', fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, axes = plt.subplots(ncols=4, figsize=(20,4))\n",
    "\n",
    "# Negative Correlations with our Class (The lower our feature value the more likely it will be a fraud transaction)\n",
    "sns.boxplot(x=\"Class\", y=\"V17\", data=new_df,  ax=axes[0])\n",
    "axes[0].set_title('V17 vs Class Negative Correlation')\n",
    "\n",
    "sns.boxplot(x=\"Class\", y=\"V14\", data=new_df,  ax=axes[1])\n",
    "axes[1].set_title('V14 vs Class Negative Correlation')\n",
    "\n",
    "\n",
    "sns.boxplot(x=\"Class\", y=\"V12\", data=new_df, ax=axes[2])\n",
    "axes[2].set_title('V12 vs Class Negative Correlation')\n",
    "\n",
    "\n",
    "sns.boxplot(x=\"Class\", y=\"V10\", data=new_df, ax=axes[3])\n",
    "axes[3].set_title('V10 vs Class Negative Correlation')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, axes = plt.subplots(ncols=4, figsize=(20,4))\n",
    "\n",
    "# Positive correlations (The higher the feature the probability increases that it will be a fraud transaction)\n",
    "sns.boxplot(x=\"Class\", y=\"V11\", data=new_df,  ax=axes[0])\n",
    "axes[0].set_title('V11 vs Class Positive Correlation')\n",
    "\n",
    "sns.boxplot(x=\"Class\", y=\"V4\", data=new_df,  ax=axes[1])\n",
    "axes[1].set_title('V4 vs Class Positive Correlation')\n",
    "\n",
    "\n",
    "sns.boxplot(x=\"Class\", y=\"V2\", data=new_df, ax=axes[2])\n",
    "axes[2].set_title('V2 vs Class Positive Correlation')\n",
    "\n",
    "\n",
    "sns.boxplot(x=\"Class\", y=\"V19\", data=new_df,  ax=axes[3])\n",
    "axes[3].set_title('V19 vs Class Positive Correlation')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.stats import norm\n",
    "\n",
    "f, (ax1, ax2, ax3) = plt.subplots(1,3, figsize=(20, 6))\n",
    "\n",
    "v14_fraud_dist = new_df['V14'].loc[new_df['Class'] == 1].values\n",
    "sns.distplot(v14_fraud_dist,ax=ax1, fit=norm, color='#FB8861')\n",
    "ax1.set_title('V14 Distribution \\n (Fraud Transactions)', fontsize=14)\n",
    "\n",
    "v12_fraud_dist = new_df['V12'].loc[new_df['Class'] == 1].values\n",
    "sns.distplot(v12_fraud_dist,ax=ax2, fit=norm, color='#56F9BB')\n",
    "ax2.set_title('V12 Distribution \\n (Fraud Transactions)', fontsize=14)\n",
    "\n",
    "\n",
    "v10_fraud_dist = new_df['V10'].loc[new_df['Class'] == 1].values\n",
    "sns.distplot(v10_fraud_dist,ax=ax3, fit=norm, color='#C5B3F9')\n",
    "ax3.set_title('V10 Distribution \\n (Fraud Transactions)', fontsize=14)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Quartile 25: -9.692722964972385 | Quartile 75: -4.282820849486866\n",
      "iqr: 5.409902115485519\n",
      "Cut Off: 8.114853173228278\n",
      "V14 Lower: -17.807576138200663\n",
      "V14 Upper: 3.8320323237414122\n",
      "Feature V14 Outliers for Fraud Cases: 4\n",
      "V10 outliers:[-18.4937733551053, -19.2143254902614, -18.0499976898594, -18.8220867423816]\n",
      "--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
      "V12 Lower: -17.3430371579634\n",
      "V12 Upper: 5.776973384895937\n",
      "V12 outliers: [-18.6837146333443, -18.5536970096458, -18.4311310279993, -18.0475965708216]\n",
      "Feature V12 Outliers for Fraud Cases: 4\n",
      "Number of Instances after outliers removal: 974\n",
      "--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
      "V10 Lower: -14.89885463232024\n",
      "V10 Upper: 4.92033495834214\n",
      "V10 outliers: [-15.5637913387301, -15.1237521803455, -15.5637913387301, -16.2556117491401, -23.2282548357516, -17.1415136412892, -16.6011969664137, -22.1870885620007, -15.2399619587112, -15.2399619587112, -15.1241628144947, -18.9132433348732, -24.5882624372475, -19.836148851696, -16.3035376590131, -15.3460988468775, -22.1870885620007, -16.6496281595399, -18.2711681738888, -22.1870885620007, -20.9491915543611, -14.9246547735487, -15.2318333653018, -16.7460441053944, -24.4031849699728, -14.9246547735487, -22.1870885620007]\n",
      "Feature V10 Outliers for Fraud Cases: 27\n",
      "Number of Instances after outliers removal: 943\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "\n",
    "# # -----> V14 Removing Outliers (Highest Negative Correlated with Labels)\n",
    "v14_fraud = new_df['V14'].loc[new_df['Class'] == 1].values\n",
    "q25, q75 = np.percentile(v14_fraud, 25), np.percentile(v14_fraud, 75)\n",
    "print('Quartile 25: {} | Quartile 75: {}'.format(q25, q75))\n",
    "v14_iqr = q75 - q25\n",
    "print('iqr: {}'.format(v14_iqr))\n",
    "\n",
    "v14_cut_off = v14_iqr * 1.5\n",
    "v14_lower, v14_upper = q25 - v14_cut_off, q75 + v14_cut_off\n",
    "print('Cut Off: {}'.format(v14_cut_off))\n",
    "print('V14 Lower: {}'.format(v14_lower))\n",
    "print('V14 Upper: {}'.format(v14_upper))\n",
    "\n",
    "outliers = [x for x in v14_fraud if x < v14_lower or x > v14_upper]\n",
    "print('Feature V14 Outliers for Fraud Cases: {}'.format(len(outliers)))\n",
    "print('V10 outliers:{}'.format(outliers))\n",
    "\n",
    "new_df = new_df.drop(new_df[(new_df['V14'] > v14_upper) | (new_df['V14'] < v14_lower)].index)\n",
    "print('----' * 44)\n",
    "\n",
    "\n",
    "# -----> V12 removing outliers from fraud transactions\n",
    "v12_fraud = new_df['V12'].loc[new_df['Class'] == 1].values\n",
    "q25, q75 = np.percentile(v12_fraud, 25), np.percentile(v12_fraud, 75)\n",
    "v12_iqr = q75 - q25\n",
    "\n",
    "v12_cut_off = v12_iqr * 1.5\n",
    "v12_lower, v12_upper = q25 - v12_cut_off, q75 + v12_cut_off\n",
    "print('V12 Lower: {}'.format(v12_lower))\n",
    "print('V12 Upper: {}'.format(v12_upper))\n",
    "outliers = [x for x in v12_fraud if x < v12_lower or x > v12_upper]\n",
    "print('V12 outliers: {}'.format(outliers))\n",
    "print('Feature V12 Outliers for Fraud Cases: {}'.format(len(outliers)))\n",
    "new_df = new_df.drop(new_df[(new_df['V12'] > v12_upper) | (new_df['V12'] < v12_lower)].index)\n",
    "print('Number of Instances after outliers removal: {}'.format(len(new_df)))\n",
    "print('----' * 44)\n",
    "\n",
    "\n",
    "# Removing outliers V10 Feature\n",
    "v10_fraud = new_df['V10'].loc[new_df['Class'] == 1].values\n",
    "q25, q75 = np.percentile(v10_fraud, 25), np.percentile(v10_fraud, 75)\n",
    "v10_iqr = q75 - q25\n",
    "\n",
    "\n",
    "v10_cut_off = v10_iqr * 1.5\n",
    "v10_lower, v10_upper = q25 - v10_cut_off, q75 + v10_cut_off\n",
    "print('V10 Lower: {}'.format(v10_lower))\n",
    "print('V10 Upper: {}'.format(v10_upper))\n",
    "outliers = [x for x in v10_fraud if x < v10_lower or x > v10_upper]\n",
    "print('V10 outliers: {}'.format(outliers))\n",
    "print('Feature V10 Outliers for Fraud Cases: {}'.format(len(outliers)))\n",
    "new_df = new_df.drop(new_df[(new_df['V10'] > v10_upper) | (new_df['V10'] < v10_lower)].index)\n",
    "print('Number of Instances after outliers removal: {}'.format(len(new_df)))\n",
    "\n",
    "\n",
    "type(new_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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J8CcRcUlEHBcRR0XEWRHxaYBiqOcc4MsR8ZqIaKOykN2zPY57E/C+iGiPiFcVfaoXYvsf4HYqtxl9Y0S8LCLeEBFvLvY/BOxZbJsQEXv3DLy4MvKDIpbfi4hW4D+pVMy/9SJ+JnOpLCJ3dUScGJU7GVxN5UP1piE+zi3AeOCjEXF4RLwTOKvHcR4CDouIE4qfxdgBnvv/gB9ExGnFz/c1EfHxiOjtKgMAEbFXRHwhIk6OiCkR8Wrgd3lxCVXS7s08sSPzhHlC0gvMEzsyT5gn6o4FJQ2HH1H5gPkHgMz8CXAGlVt/3lE8LgQernrNTODXVD7Efkjlw/ahHsf9IJUF+m4B/gv4KrC9kp2Z26gMgfw/Kh/aS4B/A8YU+39B5c4J36ZSdf9wH/G/vYjxhuLfvYEZ+cIdGQYtM5PKwoEdRfw3A2uANxf7huw4xZzv9wIXAIuoLLz3zz0OdS1wI5Wk2QGcN8Bzn07l/+grwDLge1TuYNHf/PStVO6U8R/Fa66nMqf8b3Z2TkkjlnmiB/OEeUJSN+aJHswT5ol6FIP43ZMkSZIkSZIcoSRJkiRJkqTBsaAkSZIkSZKkQbGgJEmSJEmSpEGxoCRJkiRJkqRBsaAkSZIkSZKkQRlddgBDYcKECTllypSyw5CkunTXXXc9kZkTy46jTOYJSeqdOaLCPCFJvesvT4yIgtKUKVOYP39+2WFIUl2KiN+UHUPZzBOS1DtzRIV5QpJ611+ecMqbJEmSJEmSBsWCkiSpbkTEoRFxc0QsiYj7IuIDvfQ5OSKejoiFxeNjZcQqSZIkNbIRMeVNkjRibAE+mJkLIqIZuCsifpaZ9/fo97+Z+QclxCdJkiQJRyhJkupIZq7OzAXF805gCXBwuVFJkiRJ6smCkiSpLkXEFOBVwO297H5NRNwTET+OiGNrGpgkSZIkp7xJkupPROwDXAv8VWau77F7AXBYZm6IiNOB7wOv6OUYFwAXAEyePHmYI5YkSZIaiyOUJEl1JSL2oFJMujozr+u5PzPXZ+aG4vmNwB4RMaGXfrMysz0z2ydOnDjscUuSJEmNxIKSJKluREQAXwOWZObn+ujTUvQjIk6iksuerF2UkqQyRcRDEbG4uNPn/F72R0R8PiKWR8SiiDihjDglaaSzoCSpLnV2djJ79mw2bNhQdiiqrdcBbwNeX3xRWBgRp0fEn0fEnxd9zgLujYh7gM8D52ZmlhWwJKkUp2RmW2a297LvNCpToV9BZerzl2oaWYPybzep8biGkqS6NG/ePB5++GFuvfVWzjjjjLLDUY1k5s+B2EmfK4ArahORJGk3dCbwjeJiw20RsX9EHJSZq8sObCTzbzep8ThCSVLd6ezsZOHChWQmCxcu9EqXJEmqlsBPI+Ku4gYMPR0MPFLVXlls6yYiLoiI+RExv6OjY5hCbQz+7SY1JkcoSao78+bNo2sGU2Z6pUsNb86cOaxZs6bUGNauXQvA+PHjS42jpaWFGTNmlBqDpNK9LjNXRcRLgJ9FxNLMnFe1v7eRrjtMjc7MWcAsgPb2dqdOvwj+7SY1JkcoSao7ixcvZuvWrQBs3bqVxYsXlxyRpM2bN7N58+ayw5AkMnNV8e/jwPXAST26rAQOrWofAqyqTXSNyb/dpMbkCCVJdae1tZW7776brVu30tTURGtra9khSaWqhxE5s2fPBmDmzJmlxiGpsUXEOGBUZnYWz08FLunR7Qbg/RHxHeDVwNOunzS8/NtNakyOUJJUd6ZOnUpxV3gigmnTppUckSRJqhOTgJ8Xd/q8A/jvzJzT426gNwIrgOXAV4C/KCfUxuHfblJjcoSSpLrT3NxMW1sbd911F21tbeyzzz5lhyRJkupAZq4AXtnL9iurnifwvlrG1ej8201qTBaUJNWlqVOn0tHR4RUuSZKk3YB/u0mNx4KSpLrU3NzsWi2SJEm7Cf92kxqPayhJkiRJkiRpUCwoSZIkSZIkaVAsKEmSJEmSJGlQXENJkiRJgzJnzhzWrFlT2vnXrl0LwPjx40uLAaClpYUZM2aUGoMkSWVxhJIkSZJ2K5s3b2bz5s1lhyGpSmdnJ7Nnz2bDhg1lhyKpRhyhJEmSpEEpe1TO7NmzAbyjlFRH5s2bx8MPP8ytt97KGWecUXY4kmrAgpKAyhWFa6+9lrPOOot99tmn7HAkSZIk7SY6OztZuHAhmcnChQuZNm2a3ykaWNnTosGp0bXilDcB3a8oSJIkSdJAzZs3j23btgGwbds2v1OodE6Nrg1HKInOzk4WLFhAZnL33Xd7RUGSJEnSgC1evLhbQWnx4sVOe2tg9TAix6nRteEIJXW7orB161avKEiSJEkasCOPPLJb+6ijjiopEkm1ZEFJ3HPPPf22JUmSJEmSqllQEk1NTf22JUmSJKkvy5Yt69ZeunRpSZFIqiULSuK5557rty1JkiRJfWltbd1+UbqpqYnW1taSI5JUCxaUtMOtFA888MCSIpEkSZK0u5k6dSoRAUBEMG3atJIjklQLFpTEfvvt1629//77lxSJJEmSpN1Nc3MzbW1tRARtbW3eMVpqEKPLDkDl+/Wvf92t/eCDD5YUiSTVpzlz5rBmzZqywyhV1/vvug1vI2tpaamLWyJLUj2ZOnUqHR0djk6SGogFJUmSdmLNmjWsXP0o4ybuW3YopcniL4antnSWG0jJNnasLzsESapLzc3NzJw5s+wwJNWQBaU6UW9Xv8u6Au1VX0n1atzEfTn+7FeXHYZKtuia28sOQZIkqS64hpLYa6+9urX33nvvkiKRJEmSJEm7A0co1YkyR+V0dnbyuc99bnv7ve99rwvpSZIkSZKkPllQEs3Nzey11148++yzHHvssRaTVBdTMNeuXQvA+PHjS4vBKZiSJEmS1DunvAmofGkfO3asX55VNzZv3szmzZvLDkOSJEmS1AtHKAmA0aNH09LS4ugkAeVOwezStTC8dwtpPBExA/g3oAn4amb+S4/9Y4FvACcCTwLnZOZDwxnT2rVreWbTsy7ILDZ2rCfHPl92GJIkSaVzhJIkqW5ERBPwBeA04BjgvIg4pke3dwJPZebLgcuBS2sbpSRJkiRHKEmS6slJwPLMXAEQEd8BzgTur+pzJnBx8fy/gCsiIjIzhyuo8ePHE1s6Of7sVw/XKbSbWHTN7RwwurnsMCRJkkrnCCVJUj05GHikqr2y2NZrn8zcAjwNHNjzQBFxQUTMj4j5HR0dwxSuJEmS1JgsKEmS6kn0sq3nyKOB9CEzZ2Vme2a2T5w4cUiCkyRJklRhQUmSVE9WAodWtQ8BVvXVJyJGA/sBa2sSnSSpVBFxaETcHBFLIuK+iPhAL31OjoinI2Jh8fhYGbFK0khnQUmSVE/uBF4RES+LiDHAucANPfrcAJxfPD8LuGk410+SJNWVLcAHM/No4HeA9/Vy8waA/83MtuJxSW1DbEyrV6/mX/7lX3jsscfKDkVSjVhQkiTVjWJNpPcDPwGWAN/LzPsi4pKI+MOi29eAAyNiOfA3wIXlRCtJqrXMXJ2ZC4rnnVRyRc+19lSC66+/nk2bNnHttdeWHYqkGvEub5KkupKZNwI39tj2sarnzwFn1zqujR3rWXTN7bU+bd14bt1GAPbcf1zJkZRrY8d6DjjIu7xJ9SAipgCvAnr7cH5NRNxDZdr0hzLzvl5efwFwAcDkyZOHL9AGsHr1arpugNHR0cFjjz3GpEmTSo5K0nCzoCRJ0k60tLSUHULp1mypFJQOGN3YxZQDDmr290GqAxGxD3At8FeZub7H7gXAYZm5ISJOB74PvKLnMTJzFjALoL293anTL8L111/frX3ttdfyF3/xFyVFI6lWLChJkrQTM2bMKDuE0s2ePRuAmTNnlhqHJEXEHlSKSVdn5nU991cXmDLzxoj4YkRMyMwnahlnI+kandRXW9LI5BpKkiRJknYLERFU1tJbkpmf66NPS9GPiDiJyneeJ2sXZeMZP358t/aBBx5YUiSSaskRSpIkSZJ2F68D3gYsjoiFxbaPApMBMvNKKncAfW9EbAGeBc71bqDDa9KkSaxdu3Z726nBUmOwoATMmTOHNWvWlB1Gqbref9eUhkbW0tLi9BZJkqQ6lJk/B2Infa4ArqhNRAJYsWJFt/by5ctLikRSLVlQolJMWbn6UcZN3LfsUEqTxW/CU1s6yw2kZBs7eq7pKEmSJKk/ra2tzJ8/v1tb0shnQakwbuK+HH/2q8sOQyVr5FuCS5IkSbvihBNO6FZQam9vLzEaSbXiotySJEmSpF22YMGCbu3q4pKkkatuC0oRMSMilkXE8oi4sOx4JEmSJEk7Wrx4cb9tSSNTXU55i4gm4AvAG4CVwJ0RcUNm3j8c51u7di3PbHrW6U5iY8d6cuzzZYchSZIkDVjZNxnaY4892LRpU7d2WTf78QY7Uu3U6wilk4DlmbkiMzcD3wHOLDkmSZIkSVIP++233/bnEcH+++9fYjSSaqUuRygBBwOPVLVXAsO2Yvb48eOJLZ0uyi0WXXM7B4xuLjsMSZIkacDqYUTOZZddxoYNGzjxxBM544wzyg5HUg3U6wil6GVbdusQcUFEzI+I+R0dHTUKS5IkSZLU03777cfYsWOZNm1a2aFIqpF6LSitBA6tah8CrKrukJmzMrM9M9snTpxY0+AkSZIkSS8YPXo0LS0t7LPPPmWHIqlG6rWgdCfwioh4WUSMAc4Fbig5JkmSJEmSJFGnayhl5paIeD/wE6AJ+Hpm3ldyWJIkSZIkSaJOC0oAmXkjcGOtzrexYz2Lrrm9VqerO8+t2wjAnvuPKzmScm3sWM8BB7kotyRJkiRJ/anbglIttbS0lB1C6dZsqRSUGv0OZwcc1OzvgyRJkiRJO2FBifq4zWbZZs+eDcDMmTNLjUOSJEmSJNW/el2UW5IkSZIkSXXKgpIkSdqpLVu2sGbNGjZs2FB2KJIkSaoDFpQkSdJOrVu3jk2bNjF37tyyQ5EkSVIdcA0lSZLq3Jw5c1izZk1p59+yZQsbN1Zu3nDPPffw5JNP0tTUVEosLS0trn0oSZJUBxyhJEmS+rVu3bpu7aeeeqqkSCRJklQvHKEkSVKdK3tEziWXXNKt/cwzz3hXUEmSpAbnCCVJktSvzOy3LUmSpMZjQUmSJEmSJEmDYkFJkiRJkiRJg2JBSZIkSZIkSYNiQUmSJPVr33337dbeb7/9SopEkiRJ9cK7vEmS6kJEfAZ4E7AZeBB4e2au66XfQ0AnsBXYkpnttYyzEXV2dnZrr1+/vqRIJKk+zZkzhzVr1pQdRqm63v/s2bPLDaQOtLS0lH6HVqkWLCgJgC1btvDEE0+wYcMG9tlnn7LDkdSYfgb8XWZuiYhLgb8DPtJH31My84nahdbYvMubJPVvzZo1rFz9KOMm7rvzziNUFt8sn9rS2X/HEW5jhxdd1DgsKAmAdevWsWnTJubOncub3/zmssOR1IAy86dVzduAs8qKRZKkwRo3cV+OP/vVZYehki265vayQ5BqxjWURGdnJxs3bgRg0aJFbNiwoeSIJIl3AD/uY18CP42IuyLigr4OEBEXRMT8iJjf0dExLEFKkiRJjcoRSnWizHnXTzzxwqyRzOTKK69kwoQJpcTifGNpZIuIuUBLL7suyswfFH0uArYAV/dxmNdl5qqIeAnws4hYmpnzenbKzFnALID29nbnaL0IEdFtmltElBiNpEYXETOAfwOagK9m5r/02D8W+AZwIvAkcE5mPlTrOCVppLOgpO2jk6rbZRWUJI1smTm9v/0RcT7wB8DvZx8L9WTmquLfxyPieuAkYIeCkobOUUcdxZIlS7a3jznmmBKjkdTIIqIJ+ALwBmAlcGdE3JCZ91d1eyfwVGa+PCLOBS4Fzql9tJI0sllQqhNljsr5+Mc/vsO2mTNn1j4QSQ2tuOL8EWBaZj7TR59xwKjM7CyenwpcUsMwhYtyl827SXk3qWoNOLr7JGB5Zq4AiIjvAGcC1QWlM4GLi+f/BVwREdHXhQpJ0q6xoCRJqhdXAGOpTGMDuC0z/zwiXkplSsPpwCTg+mL/aOBbmTmnrIAbxbJly7q1ly5dWlIkgkoxZfWjK5m477iyQynNaCp1gS2dT5UcSbk61m/ceaeR52DgkScTZ24AACAASURBVKr2SqDnStjb+xR3Dn0aOBDodnfQYh2+CwAmT578ooJau3Ytz2x61gWZxcaO9eTY58sOQ6oJC0qSpLqQmS/vY/sq4PTi+QrglbWMSzuOSPIif/km7juOs199fNlhqGTX3L6o7BDK0Nsibj0/lAbSx7X2JOlFsqAkSZL6NWbMGDZt2tStLUklWQkcWtU+BFjVR5+VETEa2A9YO5xBjR8/ntjSyfFn9xwspUaz6JrbOWB0c9lhSDUxquwAJElSfasuJvXWlqQauhN4RUS8LCLGAOcCN/TocwNwfvH8LOAm10+SpKFnQUmSJPVr/Pjx3doHHnhgSZFIanSZuQV4P/ATYAnwvcy8LyIuiYg/LLp9DTgwIpYDfwNcWE60kjSyOeVNkiT1a9KkSaxd+8JskZaWlhKjkdToMvNG4MYe2z5W9fw54OxaxyVJjcYRSpIkqV8rVqzo1l6+fHlJkUiSJKleOEJJTJkyhYceemh7+7d+67fKC0aSVHdaW1uZP39+t7YkqbuNHetZdM3tZYdRmufWbQRgz/3HlRxJuTZ2rOeAg1yUW43BgpLYa6+9urX33HPPkiKRJNWjE044oVtBqb29vcRoJKn+OBUY1mypFJQa/Q5nBxzU7O+DGoYFJbF06dJu7SVLlpQUiSSpHi1YsICIIDOJCObPn88ZZ5xRdliSVDdmzJhRdgilmz17NgAzZ84sNQ5JteMaSqLnXVS9q6okqdrixYu354bMZPHixSVHJEmSpLJZUBKjRo3qty1Jamytra00NTUB0NTU5BpKkiRJsqAkOO6447q1/aIgSao2depUIgKAiGDatGklRyRJkqSyWVAS06dP7/ZFYfr06SVHJEmqJ83NzbS1tRERtLW1sc8++5QdkiRJkkpmQUk0NzdzwAEHADB+/Hi/KEiSdjB16lQmT57s6CRJkiQBFpQEdHZ28vTTTwOwbt06NmzYUHJEkqR609zczMyZM73oIEmSJMCCkoB58+Z1a996660lRSJJkiRJknYHFpTE4sWL2bp1KwBbt271dtCSJEmSJKlfFpS0w13dvMubJEmSJEnqjwUlccIJJ3Rrt7e3lxSJJEmSJEnaHVhQErfddlu39i9/+cuSIpEkSZIkSbsDC0ri3nvv7dZ2DSVJkiRJktQfC0qSJEmSJEkaFAtK4sgjj+zWPuqoo0qKRJIkSZIk7Q4sKIk99tij37YkSZIkSVI1C0pi2bJl3dpLly4tKRJJkiRJkrQ7sKAkp7xJkiRJkqRBsaAkSZIkSZKkQRlddgAqn1PeJEmSpN3XnDlzWLNmTakxdJ1/9uzZpcbR0tLCjBkzSo1BahSOUBKtra2MGlX5VRg1ahStra0lRyRJkiRpdzJmzBjGjBlTdhiSasgRSmLq1KksXLiQbdu2MWrUKKZNm1Z2SJIkSZIGyBE5ksrgCCXR3NxMW1sbEUFbWxv77LNP2SFJakARcXFEPBoRC4vH6X30mxERyyJieURcWOs4JUmSJDlCSYWpU6fS0dHh6CRJZbs8Mz/b186IaAK+ALwBWAncGRE3ZOb9tQpQkiRJkiOUVGhubmbmzJmOTpJU704ClmfmiszcDHwHOLPkmCRJkqSG4wglqc7Uw1066kG93CmkbA14p5L3R8SfAfOBD2bmUz32Hww8UtVeCby6VsFJkiTVO79P+F2i2nB+n7CgJAA6Ozu59tprOeussxylVLI1a9aw+tGVTNx3XNmhlGo0CcCWzp71hMbRsX5j2SEMuYiYC7T0susi4EvAPwFZ/HsZ8I6eh+jltdnHuS4ALgCYPHnyLkYsSaoXEfEZ4E3AZuBB4O2Zua6Xfg8BncBWYEtmttcyzkbl94n64fcJv0t0Ge7vExaUBMC8efN4+OGHufXWWznjjDPKDqfhTdx3HGe/+viyw1DJrrl9UdkhDLnMnD6QfhHxFeBHvexaCRxa1T4EWNXHuWYBswDa29t7LTpJu6O1a9ey6dlnRuRnhAanY/1Gxj7fUB9vPwP+LjO3RMSlwN8BH+mj7ymZ+UTtQpPfJ+qL3ycEw/99wjWURGdnJwsXLiQzWbhwIRs2bCg7JEkNKCIOqmq+Bbi3l253Aq+IiJdFxBjgXOCGWsQnSSpXZv40M7cUzduoXFRQHfD7hNSYHKEk5s2bR2bl6lZmelVBUlk+HRFtVKawPQS8ByAiXgp8NTNPL65Kvx/4CdAEfD0z7ysrYKkM48ePZ0tneOVZXHP7IkY3H1B2GGV5B/DdPvYl8NOISODLxYjVHTg1euj4fUJqTI5QEosXL2br1q0AbN26lcWLF5cckaRGlJlvy8zWzDw+M/8wM1cX21dl5ulV/W7MzCMy8/DM/GR5EUuShlpEzI2Ie3t5nFnV5yJgC3B1H4d5XWaeAJwGvC8ipvbWKTNnZWZ7ZrZPnDhxyN9LI/H7hNSYLCiJ1tZWmpqaAGhqaqK1tbXkiCRJktSIMnN6Zh7Xy+MHABFxPvAHwP/LriExOx5jVfHv48D1wEm1ir9RHXnkkd3aRx11VEmRSKolC0pi6tSpRFRunBQRTJs2reSIJEmSpO4iYgaVRbj/MDOf6aPPuIho7noOnErva/JJkl6kuisoRcTFEfFoRCwsHqfv/FV6MZqbm2lrayMiaGtr8zafkiRJqkdXAM3Az4rvCVdCZa29iLix6DMJ+HlE3APcAfx3Zs4pJ9zGsWzZsm7tpUuXlhSJpFqq10W5L8/Mz5YdRCOZOnUqHR0djk6SJElSXcrMl/exfRVwevF8BfDKWsalyhIaCxYsYNu2bYwaNcolNKQGUXcjlFSO5uZmZs6c6egkSZIkSYMydepURo2qfLUcNWqUF6mlBlGvBaX3R8SiiPh6RPR6L9SIuCAi5kfE/I6OjlrHJ0mSJEmicnH6mGOOAeDYY4/1IrXUIEopKO3kdqBfAg4H2oDVwGW9HcPbfEqSJElSfXj++ee7/Stp5CtlDaXMnD6QfhHxFeBHwxyOJEmSJGkXdXZ2bl+Ie8mSJWzYsMFRSlIDqLspbxFxUFXzLXibT0mSJEmqW3PnziUzAchM5s6dW3JEkmqh7gpKwKcjYnFELAJOAf667IAaQWdnJ7Nnz2bDhg1lhyJJkiRpN3Lvvd3HACxevLikSCTVUt0VlDLzbZnZmpnHZ+YfZubqsmNqBPPmzePhhx/m1ltvLTsUSZIkSZJU5+quoKTa6+zsZOHChWQmCxcudJSSJEmSpAE77rjjurVbW1tLikRSLVlQEvPmzes259lRSpIkSZIGavr06UQEABHB9OkDugeTpN2cBSWxePFitm7dCsDWrVud8yxJkiRpwJqbm7ePSjr++OO9w5vUICwoidbWVpqamgBoampyiKokSZKkQZk+fTqHHXaYo5OkBmJBSUydOrXbENVp06aVHJEkSZKk3UlzczMzZ850dJLUQCwoiebmZtra2ogI2traTAKSJEmSJKlfo8sOQPVh6tSpdHR0ODpJkiRJkiTtlAUlAS8MUZUkSZIkSdoZp7xJkiRJkiRpUCwoSZIkSZIkaVAsKEmSJEmSJGlQLChJkiRJkiRpUCwoSZIkSZIkaVC8y5skSZIkSSPE2rVr2fTsM1xz+6KyQ1HJOtZvZOzzOWzHd4SSJEmSJEmSBsURSlKd8YqCugz3FQVJkiSNPOPHj2dLZ3D2q48vOxSV7JrbFzG6+YBhO74jlCRJkiRJkjQogxqhFBEPZOYRwxWMJK8o6AXDfUWhPxHRBLwLOASYk5n/V7Xv7zPzE6UEJkkqXUSMBt4JvAV4KZDAKuAHwNcy8/kSw5Mk1UifBaWI6KSSHACi+Hfvru2Zue9wBydJKs2Xgb2BO4DPR8Stmfk3xb63AhaUJKlxfRNYB1wMrCy2HQKcD/wncE45YUmSaqm/EUqzgf2Av83MxwAi4teZ+bJaBCZJKtVJmXk8QERcAXwxIq4DzuOFiwxDKiK+CxxZNPcH1mVmWy/9HgI6ga3AlsxsH454JEl9OiEzj+yxbSVwW0Q8UEZAkqTa67OglJl/GREnAt+OiO8DV/DCiCVJ0sg2putJZm4BLoiIjwE3AfsMxwkzc/sV7Yi4DHi6n+6nZOYTwxGHJGmnnoqIs4FrM3MbQESMAs4Gnio1MklSzfS7KHdm3gVML5q3AnsOe0SSpHowPyJmVG/IzEuAq4Apw3niiAjgj4FvD+d5JEm77FzgLOCxiHigGJW0hsqU6HNLjUySVDM7vctbZm7LzM9T+eP+XcMfkiSpbJn5p5k5p5ftX83MPYb59L8HPJaZv+pjfwI/jYi7IuKCvg4SERdExPyImN/R0TEsgUpSI8rMhzLznMycCLwGeG1mvqTY9uvhOm9EXBwRj0bEwuJxeh/9ZkTEsohYHhEXDlc8ktTodlpQ6pKZqzPzRoCIeMPwhSRJqmcvJgdExNyIuLeXx5lV3c6j/9FJr8vME4DTgPdFxNTeOmXmrMxsz8z2iRMn7mrIkqR+ZOaT1VOQa/A94fLMbCseN/bcWdyl9AtUcsQxwHkRccwwxyRJDam/Rbn78zVg8lAGIknabexyDsjM6f3tL25F/VbgxH6Osar49/GIuB44CZi3K/FIkoZc2d8TTgKWZ+YKgIj4DnAmcH+JMUnSiNRnQSkibuhrF3Dg8IQjSaoHJeaA6cDSzFzZ286IGAeMyszO4vmpwCXDGI8kqYeSvye8PyL+DJgPfDAzey4CfjDwSFV7JfDq3g5UTJu+AGDyZK+VS9Jg9TdC6feAPwU29NgeVCr/kqSRq6wccC49prtFxEuBr2bm6cAk4PrKut2MBr7V21pPkqRhNWw5IiLmAi297LoI+BLwT1TW0vsn4DLgHb3E0FOvd6rOzFnALID29nbvZi1Jg9RfQek24JnMvLXnjohYNnwhSZLqQCk5IDNn9rJtFXB68XwF8MrhOr8kaUCGLUfsbGp01Xm+Avyol10rgUOr2ocAq15MTJKk3vVXUFoBbO5tR2b2ugCqJGnEMAdIdaxj/UauuX1R2WGUZt3G5wDYf9yeJUdSro71Gzmo+YAyTl1KjoiIgzJzddF8C3BvL93uBF4RES8DHqUy8vVPhismSWpk/RWUHgA+GxEHAd8Fvp2ZC2sTliSpZOYAqU61tPQ2G6ixbNm4BoDR5RRT6sZBzQeU9ftQVo74dES0UZnC9hDwHug+NTozt0TE+4GfAE3A1zPzvhrEJkkNp8+CUmb+G/BvEXEYlcr+VRGxJ5W1Lb6TmQ/UKEZJUo2ZA6T6NWPGjLJDKN3s2bMBmDlzZqlxNKqyckRmvq2P7dunRhftG4EbhyMGSdILRu2sQ2b+JjMvzcxXURku+hZgybBHJkkqnTlAktQXc4QkNbadFpQiYo+IeFNEXA38mMoQ1z8a9sgkSaUzB0iS+mKOkKTG1ueUt4h4A3AecAZwB/Ad4ILM3Fij2CRJJTEHSJL6Yo6QJEH/i3J/FPgW8KHMXFujeCRJ9cEcIEnqizlCktTvotyn1DIQSVL9MAdIkvpijpAkwQDWUJIkSZIkSZKqWVCSJEmSJEnSoFhQkiRJkiRJ0qBYUJIkSZIkSdKgWFCSJEmSJEnSoFhQkiRJkiRJ0qBYUJIkSZIkSdKgWFCSJEmSJEnSoFhQkiRJkiRJ0qBYUJIkSZIkSdKgWFCSJEmSJEnSoFhQkiRJkiRJ0qCMLjsASZIkSZI0dDrWb+Sa2xeVHUZp1m18DoD9x+1ZciTl6li/kYOaDxi241tQkiRJkiRphGhpaSk7hNJt2bgGgNHDWEzZHRzUfMCw/j5YUJIkSZIkaYSYMWNG2SGUbvbs2QDMnDmz1DhGOtdQkiRJkiRJ0qBYUJIkSZIkSdKgWFCSJEmSJEnSoLiGklSHGv2uDOCdGWD478ogSZIkSbvKgpJUZ7wrQ4V3Zhj+uzJIkiRJ0q6yoCTVGe/KUOGdGSRJkiSpfrmGkiRJkiRJkgbFgpIkqaYi4uyIuC8itkVEe499fxcRyyNiWUS8sY/Xvywibo+IX0XEdyNiTG0ilyRJktTFgpIkqdbuBd4KzKveGBHHAOcCxwIzgC9GRFMvr78UuDwzXwE8BbxzeMOVJEmS1JMFJUlSTWXmksxc1suuM4HvZOamzPw1sBw4qbpDRATweuC/ik3/Abx5OOOVJNWHYlTqwuLxUEQs7KPfQxGxuOg3v9ZxSlKjKKWg9GKnO0iSRqSDgUeq2iuLbdUOBNZl5pZ++gAQERdExPyImN/R0THkwUqSaiszz8nMtsxsA64Fruun+ylF3/Z++kiSXoSy7vLWNd3hy9Ube0x3eCkwNyKOyMyttQ9RkrSrImIu0NLLrosy8wd9vayXbbkLfSobM2cBswDa29t77SNJ2v0Uo1X/mMqIVUlSSUopKGXmEoBKLuhm+3QH4NcR0TXd4Ze1jVCS9GJk5vRdeNlK4NCq9iHAqh59ngD2j4jRxSil3vpIkka23wMey8xf9bE/gZ9GRAJfLi4w7CAiLgAuAJg8efKwBCpJI1m9raE0kOkOgFMZJGkEugE4NyLGRsTLgFcAd1R3yMwEbgbOKjadD/Q14kmStJuJiLkRcW8vjzOrup0HfLufw7wuM08ATgPeFxFTe+uUmbMysz0z2ydOnDiE70KSGsOwjVAaxukOlY1OZZCk3VJEvAX4d2Ai8N8RsTAz35iZ90XE94D7gS3A+7qmPEfEjcC7MnMV8BHgOxHxCeBu4GulvBFJ0pDb2QjXiBhNZemME/s5xqri38cj4noqMx7m9dVfkrRrhm2EUmZOz8zjenn0dyV5INMdNIxmzpxJROzwWLiw15tojDhTpkzhs5/9bNlhSCNaZl6fmYdk5tjMnJSZb6za98nMPDwzj8zMH1dtP73qC8KKzDwpM1+emWcX06RVI+YJ84RUsunA0sxc2dvOiBgXEc1dz4FTqazfKkkaYmUtyt2XG4BvRcTnqCzKvcN0Bw2/6dOn881vfrPbtgkTJpQUzY42b97MmDFjSjv/tm3byEyamppKi0GSymSe6J95QhpW59JjultEvBT4amaeDkwCri/Wah0NfCsz59Q8SklqAKWsoRQRb4mIlcBrqEx3+AlAZt4HdE13mEPVdAfVztixY2lpaen2GD26UnvMTD796U9z+OGHs9dee9Ha2sp//ud/bn/tOeecw3vf+97t7YsuuoiI4Pbbb9++7ZBDDuHqq6/e3r7qqqs45phj2HPPPTniiCO4/PLL2bZt2/b9EcEXvvAF3vrWtzJu3Dg++tGP9hr3zmL75je/ybhx41i6dOn2bRdeeCGHHnooTz31FCeffDK/+c1v+Nu//dvtV9wBZs+ezT777MONN97Icccdx5gxY1iyZMmAY//Sl77EmWeeyd57780RRxzBzTffzMqVK3njG9/IuHHjaGtrY8GCBd3eyy9+8QumTZvG3nvvzcEHH8x73/te1q9fP4D/PUkafuYJ84RUlsycmZlX9ti2qigmdY1ifWXxODYzP1lOpJLUADJzt3+ceOKJqaFx/vnn5xlnnNHn/o9+9KN5xBFH5I9//ONcsWJFXn311bn33nvnj370o8zM/OIXv5hHHnnk9v6vfe1rc8KECfmpT30qMzMfeOCBBHLlypWZmTlr1qxsaWnJa665JlesWJE33HBDTpo0Kf/93/99+zGAnDhxYn7lK1/JBx98MFesWLFLsWVmnnfeednW1pabNm3Km2++OUePHp033XRTZmY++eSTecghh+THPvaxXL16da5evTozM6+66qpsamrK17zmNfnzn/88ly1bluvXrx9w7C996UvzW9/6Vj7wwAN53nnn5aRJk/LUU0/N73//+7ls2bI87bTTsrW1dftrFi1alOPGjcvPfvaz+cADD+Rtt92Wv/M7v5N/9Ed/NLD/xBHiqquuyquuuqrsMEYEYH7WwWd1mQ/zxNAxT5gn6oE5YuiYI8wT0khknhg6/eWJ0j+8h+JhAhg6559/fjY1NeW4ceO2P2bMmJGZmRs2bMg999wz582b1+01H/jAB/K0007LzMz7778/gVy1alVu3Lgxx4wZk5/61Kfy1FNPzczKF4OXv/zl21976KGH5je+8Y1ux7v88svz6KOP3t4G8v3vf3+/cQ8ktszMdevW5WGHHZbvfve785BDDskPf/jD3fofdthh+ZnPfKbbtquuuiqBnD9/frftA439wgsv3N5evHhxAnnZZZdt33bzzTcnkB0dHZmZ+ba3vS3f8Y53dDvu3XffnUA+9thj/f4cRhKTwNDxy4J5YiiZJ8wT9cAcMXTMEeYJaSQyTwyd/vJEva2hpDowdepUZs2atb291157AXD//ffz3HPPMWPGjO3D/AGef/55pkyZAsDRRx/NpEmTuOWWW5gwYQKHH3445557Lp/4xCd4/vnnueWWWzj55JMB6Ojo4JFHHuE973lPt+kPW7ZsofJ7+4L29vZ+Yx5IbAD77bcfs2fP5pRTTqGtrY1PfOITA/qZjB49mra2tu3twcR+/PHHb38+adIkAFpbW3fY9vjjjzNhwgTuuusuli9fzne/+93tfbqO+eCDD/KSl7xkQDFL0nAxT+zIPCFJkhqNBSXtYO+99+blL3/5Dtu71nz44Q9/yOTJk7vt22OPPbY/nzZtGjfffDMTJ07klFNOYcqUKUyYMIE777yTW2+9lUsvvbTb8a688kpe+9rX9hvTuHHj+t0/0NgA/vd//5empiYee+wx1q9fz4EHHtjvsaGyXkj14qqDib36/F1fYnrb1nXMbdu28a53vYu//uu/3uFYBx988E5jlaThZp7YkXlCkiQ1GgtKGrBjjjmGsWPH8pvf/IbXv/71ffY7+eST+dznPsdLXvIS/uqv/gqofHmYNWsWjz766PYrz5MmTeLggw/mwQcf5M/+7M9qEtsdd9zBP/3TP3HdddfxiU98gne/+91cd9112/ePGTOGrVt3vg78UMbe0wknnMB9993X65c1Sapn5okXmCf+//buPD6q+t7/+PuTQBZC2FKWVCgoAoLIVn4o9RYq1da6lhJExLCUVcBrUaTg1bqALQrVcitQRCiroMK114qoxbak9ZYqm4igVSggGBYFFDLZ8/39kSFmSDIQksyZzLyej8c8Muc733PmPTwCX+Zzzvd7AABApKOghPOWnJysyZMna/LkyXLOqU+fPjp9+rQ2bdqkmJgYjRkzRlLxF4Xx48dr3759JV8Kvve972n06NG69NJLA86ePvLII7r77rvVqFEj3XDDDcrPz9fWrVt16NAhTZs2rVqznT59WkOGDNG4ceN0yy23qGPHjurevbsWLVqkkSNHSpLatGmjv/3tb7rzzjsVHx8f9DbY1ZX9bD//+c911VVXady4cRo7dqySk5P14Ycf6o9//KMWLFhwwccFgJrGOBGIcQIAAEQyCkqolOnTp6t58+aaPXu27rrrLjVo0EDdunXTlClTSvp07NhRLVq0UEpKipo2bSpJuuaaa1RYWFjyxeGMUaNGKSkpSbNmzdK0adOUmJioyy+/XBMnTqz2bPfcc4/i4uL05JNPSpLatWunOXPm6J577lGfPn3Url07PfbYYxo7dqzatm2r3NzcMutc1FT20rp06aKMjAw9+OCD6tu3rwoLC3XJJZeof//+VTouAIQC40TNZC+NcQIAAIQDC/YfodqiZ8+ebvPmzV7HAFCNlixZIkkaPny4pzkigZltcc4FX7E4wjFOAJGFMaL6MEYUY5wAIgvjRPUJNk7EhDoMAAAAAAAAajcKSgAAAAAAAKgUCkoAAAAAAACoFApKAAAAAAAAqBQKSgAAAAAAAKgUCkrwzL59+2RmOnNHjbO3AQDRjXECAAAgfFFQQkh873vf08SJE4P2adWqlTIzM9WtW7cQpQIAhAvGCQAAgNqFghLCRmxsrFq0aKE6depc8DHy8vKqMREAIJwwTgAAAIQPCko4p4yMDF155ZVKSEhQ8+bNNWnSpID/kJd3Vnn48OG66aabSp5v3LhRc+fOlZnJzLRv374y71PeVIZdu3bpxhtvVHJyspo1a6bBgwfr8OHDZd7niSeeUMuWLdWyZUtJ0v/8z/+oS5cuSkxMVJMmTdS3b18dOXKkOv9YAAB+jBMAAADRh4ISgjp06JB+9KMfqXv37tq2bZsWLVqkVatWadq0aed9jDlz5qh3794aMWKEMjMzlZmZqVatWp1zv8zMTPXp00edO3fWO++8ow0bNuj06dO65ZZbVFRUVNJv48aN2rFjh15//XW99dZbOnz4sG6//XYNGzZMu3fvVkZGhtLT0y/o8wMAgmOcAAAAiE4Xfs04osK8efOUmpqqefPmKSYmRh07dtTMmTM1duxYTZ8+XfXq1TvnMRo2bKi4uDjVq1dPLVq0OO/3nj9/vrp27aonnniipG3ZsmVq0qSJNm/erF69ekmSEhIStHjxYsXHx0uStm7dqvz8fKWlpal169aSpM6dO1fmYwMAzhPjBAAAQHTiCiUEtXv3bvXu3VsxMV//qvzHf/yH8vLy9Mknn9Toe2/ZskUZGRmqX79+yePMGes9e/aU9OvcuXPJlwRJ6tq1q6699lp17txZAwYM0Pz583Xs2LEazQoA0YpxAgAAIDpxhRKCcs7JzMp97Ux7TEyMnHMBr+Xn51f5vYuKinTjjTdq9uzZZV5r3rx5yfOkpKSA12JjY/Xmm29q06ZNevPNN7Vo0SJNmzZNGzduVNeuXaucCwDwNcYJAACA6MQVSgiqU6dO+sc//hGwFsXf//53xcXFqW3btpKkpk2bKjMzM2C/9957L2A7Li5OhYWFlXrvHj166IMPPlDr1q116aWXBjySk5OD7mtm6t27tx5++GG9++67+uY3v6kXXnihUu8PADg3xgkAAIDoREEJQY0fP16fffaZxo8fr927d2vdunWaOnWqJk6cWLIuRr9+/bR+/Xq98sor+uijj3Tvvffq008/DThOmzZt9M4772jfvn36/PPPA754VGTChAn6p8xulAAAIABJREFU8ssvNWjQIP3zn//U3r17tWHDBo0ZM0anTp2qcL9NmzZpxowZevfdd3XgwAG98sor+vTTT9WpU6eq/WEAAMpgnAAAAIhOFJQQ1EUXXaT169dr27Zt6tatm376059q8ODB+uUvf1nS56c//WnJ4+qrr1b9+vXVv3//gONMnjxZcXFx6tSpk5o2baoDBw6c872/+c1v6u2331ZMTIyuv/56XX755ZowYYLi4+MD1sI4W8OGDfX222/rpptuUrt27XTffffpoYce0p133nnhfxAAgHIxTgAAAEQnO3tNg9qoZ8+ebvPmzV7HAFCNlixZIkkaPny4pzkigZltcc719DqHlxgngMjCGFF9GCOKMU4AkYVxovoEGye4QgkAAAAAAACVQkEJiBDHjx/XHXfcodWrV3sdBQAQhj799FMNGTJEO3fu9DoKAACIABSUgAjw6quv6tJLL9WaNWs0evToMovdAuHEzAaa2QdmVmRmPUu1X2dmW8zsff/PfhXs/4iZHTKz7f7HDaFLD9Q+hYWFmjNnji677DK98MILuu222yp9Rz0AAICzUVACarmxY8dq0KBBOnHihPLz85Wdna1BgwYpEtZHQ8TaKeknkjLOav9c0s3OuSskDZO0PMgxnnbOdfM/XquhnECt5/P51KNHDz3wwAPy+XwqLCzU/v379fTTT3sdDQAA1HIUlIBa7sSJEwFnmgsLC7Vjxw7NnTvXw1RAxZxzu51zH5XTvs0595l/8wNJCWZW8a26AJxT3bp1dfLkSfl8vpI2n8+nhx9+WPv37/cwGVCxiq5k9b82zcw+MbOPzOyHFex/sZn908w+NrMXzCwuNMkBILpQUAJquQULFigpKSmgLSsrSz//+c+1Z88ej1IBVTZA0jbnXG4Fr080sx1mttjMGocyGFCb1K1bV88//7wSExMD2nNzczV06FCuZkW4KvdKVjPrJOl2SZdLul7SPDOLLWf/J1R8JWs7SSckjazZuAAQnSgoAbVc48aNtWLFCtWrVy+gPScnRwMHDmSdDHjCzDaY2c5yHreex76Xq/jLwNgKusyX1FZSN0mZkn5dwXHGmNlmM9t87NixC/wkQO139dVX6/bbb1d8/NcX/BUWFmrLli16/vnnPUwGlK+iK1kl3SpptXMu1zn3b0mfSOpVuoOZmaR+ktb4m5ZK+nFN5gWAaEVBCYgAP/rRjzRgwAAlJCSUtBUVFelf//qXZs+e7WEyRCvn3LXOuc7lPP432H5m1lLSy5KGOufKvcTOOXfEOVfonCuStFBnfZko1e9Z51xP51zPpk2bVvUjAbXa008/Xe7VrOPHj9cXX3zhUSqg0i6SVPrOIwf9baWlSDrpnCsI0kcSJx4AoKooKAERYu7cuWrQoEFAW1ZWlh599FHt2rXLo1TA+TOzRpLWSZrmnHs7SL/UUpv9VTw1AkAQDRs21HPPPVfu1azjx4/3KBWi2QVeyWrltJ09b/N8+hQ3cuIBAKqEghIQIZKTk/Xiiy+WWScjJydHaWlpys/P9ygZEMjM+pvZQUm9Ja0zszf8L02UdKmkh8xsu//RzL/Pc6UWZn3SzN43sx2SrpE0KdSfAaiN+vfvrz59+qhOnTolbXl5eXr11Ve1YcMGD5MhGl3glawHJbUqtd1S0mdn9flcUiMzqxOkDwCgGlBQAiJI3759NWzYsICiknNO+/fv1/Tp0z1MBnzNOfeyc66lcy7eOdfcOfdDf/sM51ySc65bqcdR/2ujnHOb/c/TnXNXOOe6OOducc5levl5gNpk8eLFAdOjpeK7vqWnpwfcCQ4IU69Iut3M4s3sYkntJL1TuoMrXmn+L5LS/E3DJAWdbg0AuDAUlIAI89RTTyklJSWgzefzafbs2dq2bZtHqQAA4SA1NVVPPvlkmfWUvvzySz3wwAMepQICVXQlq3PuA0kvStol6XVJE5xzhf59XjOzb/oP8XNJ95rZJypeU2lRqD8DAEQDCkpAhElMTNSaNWvKTH3Lzs7WgAEDlJtb0V3YAQDRYOzYsWrfvr2Kb4ZVLDs7W88++ywnHhAWKrqS1f/a4865ts65Ds659aXab3DOfeZ/vtc518s5d6lzbqBzjv/8AEANoKAERKArr7xSEyZMKLP46uHDhzkDDQBRLiYmRqtWrSoz9S07O1uDBw9WQUFBBXsCAAB8jYISEKFmzJih1NTUgLbs7GzNnz9f//jHPzxKBQAIBx06dND9999f5sTDp59+ql//+tcepQIAALUJBSUgQsXHx2vt2rXlTn1LS0tj8VUAiHL/9V//pWbNmgW0+Xw+PfbYY/r3v//tUSoAAFBbUFACIljXrl01ZcqUMmegjx8/rkmTuNM6AESzuLg4rVq1qsyJh9zcXKWnp6v4ZlkAAADlo6AERLgHH3xQbdq0CVh8NScnR8uXL9df/vIXD5MBALx21VVXaciQIQHrKRUWFmr79u1avny5h8kAAEC4o6AERLg6depo7dq15S6+OmjQIH311VceJQMAhIOnnnpKSUlJAW1ZWVm6++67dezYMY9SAQCAcEdBCYgCl112mR599NEyU9+++uorjR8/3qNUAIBwkJycrN///vdlxoicnByNGzfOo1QAACDcUVACosR9992njh07Kibm67/2ubm5evnll7Vu3ToPkwEAvHbzzTerX79+qlu3bklbXl6eXn/9db3xxhseJgMAAOGKghIQJWJiYvTiiy+Wmfrm8/mUnp6u48ePe5QMABAOFi5cqPj4+IA2n8+noUOHKisry6NUAAAgXFFQAqLIJZdcolmzZpW7VsbIkSM9SgUACActWrQodz2lr776SlOnTvUoFQAACFcUlIAoc9ddd6lbt26qU6dOSVteXp7efPNNvfTSSx4mAwB4bdSoUerUqVPA9OicnBwtWrRIW7Zs8TAZAAAINxSUgChjZlq9enW5U99GjRqlI0eOeJQMAOA1M9PKlSvLTH3Lzs7W4MGDlZ+f71EyAAAQbigoAVGoZcuWeuaZZ8pMa8jOzlZ6erqccx4lAwB4rV27dpo2bVqZu74dOnRIs2bN8igVAAAINxSUgCg1dOhQXX311QF39MnPz9fbb7+tZcuWeZgMAOC1qVOnKjU1NaDN5/NpxowZ2rNnj0epAABAOKGgBEQpM9OyZcuUmJgY0O7z+TRx4kQdPHjQo2QAAK/VrVtXzz//fJkxIjc3V3feeSdXsgIAAApKQDRr3ry5nnvuuTLTGnJycnT77bfzhQEAolivXr00bNiwgDX3ioqK9P7772vJkiXeBQMAAGGBghIQ5QYOHKgf/OAHiouLK2krKCjQ9u3bNX/+fA+TAQC8NmvWLCUnJwe0ZWVl6Z577tHRo0c9SgUAAMIBBSUAWrRoUZkFurOysnT//fdr7969HqUCAHitfv36WrJkSblXso4ZM8ajVAAAIBxQUAKgJk2aaPny5eV+YRg4cKCKioo8SgYA8NoNN9yg6667rsxNHP70pz/ptdde8zAZAADwEgUlAJKkG2+8Uf3791d8fHxJW1FRkT788EPNnj3bw2QAAK89++yzAWspScU3cRg+fLhOnz7tUSoAAOAlCkoASsybN08NGjQIaPP5fHrkkUe0e/duj1IBALzWrFkzzZkzp8z06FOnTun+++/3KBUAAPASBSUAJRo0aKAXXnihzG2ic3JylJaWpoKCAo+SAQC8Nnz4cF1xxRWKifn6v485OTlaunSp3nnnHQ+TAQAAL1BQAhDgmmuuUXp6ekBRyTmnffv2acaMGR4mAwB4ycy0YsWKgKnRkpSdna077rhD+fn5HiUDAABeoKAEoIynn35ajRs3Dmjz+Xx68skntX37do9SAQC81rZtWz344INlbuKQmZmpmTNnepQKAAB4wZOCkpkNNLMPzKzIzHqWam9jZtlmtt3/+J0X+YBoV69ePa1Zs6bM1Lfs7GylpaUpNzfXo2QAAK9NmTJFF110UUCbz+fTr371K3388ccepQIAAKHm1RVKOyX9RFJGOa/tcc518z/GhTgXAL/evXvrrrvuKvcs9IMPPuhRKgCA1+rUqaNVq1aVOemQm5urIUOGyDnnUTIAABBKnhSUnHO7nXMfefHeAM7fL3/5SzVv3jygzefzae7cufrnP//pUSoAgNe+/e1va+TIkUpISChpKyoq0q5du7Ro0SIPkwEAgFAJxzWULjazbWa20cy+W1EnMxtjZpvNbPOxY8dCmQ+IGvHx8Vq7dm25U98GDBggn8/nUTIAgNdmzpypBg0aBLRlZWVp0qRJOnz4sEepAABAqNRYQcnMNpjZznIetwbZLVPSt5xz3SXdK+l5M2tQXkfn3LPOuZ7OuZ5NmzatiY8AQFL37t01efLkMlPfjh8/rvvuu8+jVKjNqrqOnpk1MbM/mdnH/p+Ny+sHoGYlJSVp+fLlZcaH3NxcjR492qNUAAAgVGqsoOScu9Y517mcx/8G2SfXOfeF//kWSXskta+pjADOz0MPPaTWrVvLzErasrOztXTpUv31r3/1Lhhqq6quozdV0lvOuXaS3vJvA/DAD37wA11//fWKi4sracvPz9ef//xn/fGPf/QwGWqrICcdrjOzLWb2vv9nvwr2f8TMDpU6OXFD6NIDQHQJqylvZtbUzGL9zy+R1E7SXm9TAahbt67WrFkTsFaGVFxUGjRokE6dOuVRMtRG1bCO3q2SlvqfL5X046qnAnChFixYUGZ88Pl8GjFiBOMDLkRFJx0+l3Szc+4KScMkLQ9yjKdLnZx4rYZyAkDU86SgZGb9zeygpN6S1pnZG/6X+kjaYWbvSVojaZxz7rgXGQEE6tSpkx5++OEyUxu++uorTZgwwaNUiEDns45ec+dcpiT5fzYrrxNr7QGh8Y1vfEO//e1vlZSUFNCelZXF1GhUWkUnHZxz25xzn/k3P5CUYGbxoU0HACjNq7u8veyca+mci3fONXfO/dDfvtY5d7lzrqtzrodzjmulgTAyefJkdejQQTExX//TkZOTo7Vr12r9+vUeJkO4qel19M4Ha+0BoZOenq7u3bsrNja2pC0nJ0crVqzQpk2bPEyGCDVA0jbnXG4Fr080sx1mtjjYOnuceACAqgmrKW8AwltsbKxeeumlcqc23HnnnTpx4oRHyRBuangdvSNmlipJ/p9Ha+IzADh/ZqZly5YpPj7wgpHs7GwNHjxYeXl5HiVDOLrAkw5n9r1c0hOSxlbQZb6ktpK6qfhExa8rOhYnHgCgaigoAaiUtm3b6oknnigzteH06dMaOXKkR6kQCSqxjt4rKl4/Q/6fFRapAITOxRdfrF/84hdlpkYfPXpUjz/+uEepEI4u5KSDJJlZS0kvSxrqnNtTwbGPOOcKnXNFkhZK6lX9nwAAIFFQAnABJkyYoK5duwZMbcjLy9Mbb7yhNWvWeJgMtcGFrKNnZs+VutvPTEnXmdnHkq7zbwMIA/fdd59atWoV0Obz+TRr1ix99FFV1uJHtDOzRpLWSZrmnHs7SL/UUpv9VbzINwCgBlBQAlBpZqbVq1eXO/Vt5MiROnqUGUio2IWso+ecG+Wc2+x//oVz7vvOuXb+n9y8AQgTderU0erVq5WYmBjQnpOToyFDhqioqMijZKgtgpx0mCjpUkkPmdl2/6OZf5/SJx2eNLP3zWyHpGskTQr1ZwCAaEFBCcAFadWqVbl39cnOzlZ6erqccx4lAwB4qVu3bhozZkxAUck5pw8//FALFy70MBlqgyAnHWY455Kcc91KPY76Xyt90iHdOXeFc66Lc+6WM3cFBRBdcnNzdeDAAR05csTrKBGtjtcBANRew4cP18qVK5WRkaH8/HxJUn5+vv7+979r+fLlGjp0qMcJAQA14fXXX9fhw4crfL1jx46Ki4tTdnZ2SVtWVpbuueceZWdnq1GjRlV6/8zMTOXn52vRokUB069DrUWLFrr++us9e38AQPmOHTsm55xeeuklTZw40es4EYuCEoALZmZasWKFOnToUFJQkoqnvk2YMEH9+vVTy5YtPUwIAPBCfHy8Ro8erf/+7/8OuMNbfn6+Fi9erHvvvbdKx3fOyTmnkydPKiUlpapxAQDV6FwnHWpabm6uCgsLJUlffPGFnn32WcXFxXmSJdJPPFBQAlAlLVq00MKFCzVixAj5fL6S9pycHA0ePFgZGRkyMw8TAgCq2/n+5/jAgQP6wx/+UFJUKioq0p49e9SoUSP9+Mc/vqD3PnXqlObMmSOpeKxJS0tT/fr1L+hYAIDIc+zYsYDto0ePcpK7hlBQAlBlt912m1auXKnXX3+95EtDQUGBtm3bpt/97ne66667PE4IAPDCvHnzAsYG6esbOPTr108NGjSo9DEzMjJKFvcuLCzUxo0bdeONN1ZbZgBA1Xh9Rc6jjz4asF1YWKjhw4d7EybCsSg3gGqxePFi1atXL6AtKytLkydP1t69ez1KBQDwUkpKiubNm1fmBg5ZWVn62c9+dkHH3LFjR8mNH5xz2rFjR5VzAgCAyqOgBKBapKSkaNmyZWWKSjk5Obrtttu4VTQARKk77rhD3/72twMWz87NzdXq1av19ttvV/p4DRs2DLoNAABCg4ISgGpz880369Zbb1V8fHxJW1FRkXbv3q2nnnrKw2QAAK+YmZYtWxYwNkhSdna27rjjDuXm5lbqeCdPngy6DQAAQoM1lACUUZU7M3z3u9/Vq6++GvAFwefz6YEHHlB+fr5SU1PP6zhn3n/JkiUXlKM6RPpdGQAgVFq3bq3HHntMv/jFLwJu4PD5559r+vTpmjFjxnkfq1GjRgELrjZq1KhaswIAgPPDFUoAqlViYqLGjRununXrBrQXFBTomWeeKbmF57nExcV5dntPAED1+9nPfqY2bdoEtPl8Pj311FPavXv3eR/nyy+/DLoNAIhuZ59oaNy4sUdJIh9XKAEoozquyjlx4oRWrFihnJwcScULp548eVIHDx7Uww8/XOXjAwBql9jYWK1atUpXXXWVsrOzS9pzcnI0ePBgbd26VTEx5z7X2aVLF23evDlgGwCAM2666SatWLGiZPvmm2/2ME1k4wolADXiN7/5TZmzAT6fT0888YTee+89ScVFpvnz56tXr15eRAQAhFiXLl00fvx4JSYmlrQ55/TJJ59o3rx553WMHj16BGz37NmzWjMCAGq3LVu2BGyXPgmB6kVBCUCNSEpK0po1awK+NEjFi7AOGDBA//73v9WnTx/df//92rZtm06cOOFRUgBAKE2fPr3MdISsrCxNnTpVhw4dOuf+W7duDdjmiwIAoLSzp1Hv2rXLoySRj4ISgBrzne98R2PGjClTVMrMzFT79u21adMmZWVlqV69etq7d69HKQEAoZSYmKiVK1eWGRtyc3M1YsSIc+7//vvvB90GAAChQUEJQI2aOXOmmjdvHtDm8/lUUFCggoICScXTHSgoAUD0uOaaa9S/f3/Fx8eXtBUUFOj//u//tHbt2qD7dujQIWD7sssuq5GMAIDaqUmTJgHbKSkpHiWJfBSUANSohIQErV27VgkJCRX2yc7O1ieffBLCVAAArz3zzDNlxoasrCyNHj1aJ0+eDGh3zoUyGgCgFktLSwvYHjhwoEdJIh8FJQA16sSJE5oxY0bQO/cUFBTogw8+CGEqAIDXGjdurAULFigpKSmg3efz6T//8z8lFReSFi5cqEaNGmnHjh2SpA8//DCg/9lrZQAAoltqaqri4uIkSXFxcWVmS6D6UFACUGPWr1+vtm3bat26dfL5fEH7nv0F4dSpU1qyZIlOnz5dkxEBAB667bbb1KtXL9WpU6ekLTc3V2vWrNGSJUt05ZVXatKkScrPz9fHH38sSWrYsGHAMc7eBgBEt1OnTikvL0+SlJeXx/eJGkRBCUCNmTJlirKyskr+QQ9m//79AdsZGRk6cOCANm7cWFPxAAAeMzMtXbo0YC0lqXgq9OjRo7VlyxZlZWWpqKhIn332mSTpyy+/DOh79jYAILpt2LAh6DaqDwUlADVm8+bNmj59upKSksp8WTjbiRMnlJ+fL6n4rML27dvlnNP27ds5qwAAEaxVq1Z6/PHHy0x9KygoUFFRkaTiq5bOnHg4exHujh07hiYoAKBW2LlzZ8A2dwOtORSUANSY+Ph4TZkyRfv379ewYcOUkJBQ4VpKCQkJOnDggKTiq5POLMDqnOMqJQCIcCNGjAiY9laePXv2hCgNAKA2O3MyoqJtVB8KSgBqXEpKihYsWKD33ntP3//+91WvXr0yferUqaO9e/dKKj6LUFhYKEkqLCzkrAIARLB169apbdu2ys3NDdrvzEmHjz76KKD97DX4AADRzcyCbqP6UFACEDLt27fXm2++qddee00dOnQImN6Ql5dXcvb5iiuuKPmH38x0xRVXeJIXAFCzRowYobS0NH3++efKyckJ2vfw4cOSVGZMYIwAAJR29rjQpUsXj5JEPgpKAEKub9++2rVrl+bOnauUlBQlJiYqOzu75Kxzjx49Aqa89ezZ08u4AIAacvXVVys2Nrbk9s7BfPHFF5LKrqHUqVOnGskGAKidrr322qDbqD4UlAB4IiYmRsOGDdOBAwc0ZcoUxcXF6V//+pckaevWrQF9N2/e7EVEAEANGzVqlPbu3auf/OQnSkxMDNq3qKhIp06d0htvvBHQvn79+pqMCACoZc6+oU9WVpZHSSIfBSUAnqpXr54eeeQR7d+/X7NmzZIk7dixI6DP2dsAgMjRrFkzrVq1Sm+99ZbatWtX5m5vZ8THx+vQoUM6duxYQPvZ2wCA6Pbyyy8HbK9du9ajJJGPghKAsNCiRYuSaQv169cPeC05OdmLSACAEOrdu7d2796tX/3qV6pfv36ZaXCxsbE6dOhQmfbzmS4HAIgenHgIHQpKAMLOyZMnA7ZPnDjhURIAQCjFxsbq7rvv1t69e5WWlhYwDa6wsFCfffaZ8vLyAvY5exsAEN0SEhKCbqP6UFACAABAWGnatKlWrlypP//5z2rfvr2SkpLk8/l08OBBr6MBAMJcYWFh0G1UHwpKAMJO586dA7a5JTQARKerrrpKu3bt0syZM5WQkKBDhw6pSZMmAX1SUlI8SgcACEddu3YNuo3qQ0EJQNi59tprZWaSJDPjVp8RxswGmtkHZlZkZj1LtQ8xs+2lHkVm1q2c/R8xs0Ol+t0Q2k8AIJRiY2M1ceJEHThwQA899JDS0tICXh84cKBHyVBTgowTbcwsu9S//7+rYP8mZvYnM/vY/7Nx6NID8FqfPn0UE1Nc6oiNjVXfvn09ThS5KCgBCDvJycklVyV16dKlzCLdqPV2SvqJpIzSjc65lc65bs65bpLSJe1zzm2v4BhPn+nrnHuthvMCCAMpKSlq3ry5UlNTS65SOtOGiFPuOOG3p9S//+Mq2H+qpLecc+0kveXfBhAlkpOT1aNHD5mZunfvzneJGkRBCUBYuvbaa9W6dWuuTopAzrndzrmPztFtsKRVocgDoPZJS0tTfHw8VydFqPMcJ4K5VdJS//Olkn5c9VQAapM+ffroW9/6Flcn1TAKSgDCUnJysoYPH84Zheg1SMELShPNbIeZLa5oKoOZjTGzzWa2mdvFApElNTVVU6dO5eqk6HSxmW0zs41m9t0K+jR3zmVKkv9ns/I6MU4AkYvvEqFBQQkAUO3MbIOZ7Szncet57HulJJ9zbmcFXeZLaiupm6RMSb8ur5Nz7lnnXE/nXM+mTZte6EcBANSACxwnMiV9yznXXdK9kp43swYXmoFxAgCqpo7XAQAAkcc5V5W5ircryNVJzrkjZ56b2UJJr1bhvQAAHriQccI5lysp1/98i5ntkdRe0uazuh4xs1TnXKaZpUo6WuXAAIAyuEIJABA2zCxG0kBJq4P0SS212V/Fi7cCACKcmTU1s1j/80sktZO0t5yur0ga5n8+TNL/hiYhAEQXCkoAgJAys/5mdlBSb0nrzOyNUi/3kXTQObf3rH2eK3Xr6CfN7H0z2yHpGkmTQhIcABASQcaJPpJ2mNl7ktZIGuecO+7fp/Q4MVPSdWb2saTr/NsAgGrGlDcAQEg5516W9HIFr/1V0lXltI8q9Ty9xsIBADxX0TjhnFsraW0F+5QeJ76Q9P0aCwgAkMQVSgAAAAAAAKgkCkoAAAAAAACoFApKAAAAAAAAqBQKSgAAAAAAAKgUc855naHKzOyYpP1e54gA35D0udchgFL4nawerZ1zTb0O4SXGiWrD30mEE34fq0fUjxES40Q14u8lwgm/j9WjwnEiIgpKqB5mttk51/PcPYHQ4HcSCC/8nUQ44fcRCD/8vUQ44fex5jHlDQAAAAAAAJVCQQkAAAAAAACVQkEJpT3rdQDgLPxOAuGFv5MIJ/w+AuGHv5cIJ/w+1jDWUAIAAAAAAEClcIUSAAAAAAAAKoWCEgAAAAAAACqFghIkSWZ2vZl9ZGafmNlUr/MgepnZYjM7amY7vc4C4GuMEwgXjBNAeGKcQLhgnAgdCkqQmcVKmivpR5I6SRpsZp28TYUotkTS9V6HAPA1xgmEmSVinADCCuMEwswSMU6EBAUlSFIvSZ845/Y65/IkrZZ0q8eZEKWccxmSjnudA0AAxgmEDcYJICwxTiBsME6EDgUlSNJFkj4ttX3Q3wYAgMQ4AQAIjnECiEIUlCBJVk6bC3kKAEC4YpwAAATDOAFEIQpKkIrPILQqtd1S0mceZQEAhB/GCQBAMIwTQBSioARJeldSOzO72MziJN0u6RWPMwEAwgfjBAAgGMYJIApRUIKccwWSJkp6Q9JuSS865z7wNhWilZmtkvQPSR3M7KCZjfQ6ExDtGCcQThgngPDDOIFwwjgROuYcU1sBAAAAAABw/rhCCQAAAAAAAJVCQQkAAAAAAACVQkEJAAAAAAAAlUJBCQAAAAAAAJVCQQkAAAAAAACVQkEJOA9m1sLMVpvZHjPbZWavmVl7M9vpdTYAgPcUwtD7AAACFElEQVQYJwAAwTBOIBLV8ToAEO7MzCS9LGmpc+52f1s3Sc09DQYACAuMEwCAYBgnEKm4Qgk4t2sk5TvnfnemwTm3XdKnZ7bNrI2Z/c3Mtvof3/G3p5pZhpltN7OdZvZdM4s1syX+7ffNbFLoPxIAoBoxTgAAgmGcQETiCiXg3DpL2nKOPkclXeecyzGzdpJWSeop6Q5JbzjnHjezWEn1JHWTdJFzrrMkmVmjmosOAAgBxgkAQDCME4hIFJSA6lFX0jP+S1cLJbX3t78rabGZ1ZX0B+fcdjPbK+kSM/utpHWS3vQkMQAglBgnAADBME6g1mHKG3BuH0j69jn6TJJ0RFJXFZ9JiJMk51yGpD6SDklabmZDnXMn/P3+KmmCpOdqJjYAIEQYJwAAwTBOICJRUALO7c+S4s1s9JkGM/t/klqX6tNQUqZzrkhSuqRYf7/Wko465xZKWiSph5l9Q1KMc26tpIck9QjNxwAA1BDGCQBAMIwTiEhMeQPOwTnnzKy/pN+Y2VRJOZL2SfpZqW7zJK01s4GS/iIpy9/+PUn3m1m+pNOShkq6SNLvzexMQXdajX8IAECNYZwAAATDOIFIZc45rzMAAAAAAACgFmHKGwAAAAAAACqFghIAAAAAAAAqhYISAAAAAAAAKoWCEgAAAAAAACqFghIAAAAAAAAqhYISAAAAAAAAKoWCEgAAAAAAACrl/wPVfT/ssB+3JAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f,(ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(20,6))\n",
    "\n",
    "colors = ['#B3F9C5', '#f9c5b3']\n",
    "# Boxplots with outliers removed\n",
    "# Feature V14\n",
    "sns.boxplot(x=\"Class\", y=\"V14\", data=new_df,ax=ax1, palette=colors)\n",
    "ax1.set_title(\"V14 Feature \\n Reduction of outliers\", fontsize=14)\n",
    "ax1.annotate('Fewer extreme \\n outliers', xy=(0.98, -17.5), xytext=(0, -12),\n",
    "            arrowprops=dict(facecolor='black'),\n",
    "            fontsize=14)\n",
    "\n",
    "# Feature 12\n",
    "sns.boxplot(x=\"Class\", y=\"V12\", data=new_df, ax=ax2, palette=colors)\n",
    "ax2.set_title(\"V12 Feature \\n Reduction of outliers\", fontsize=14)\n",
    "ax2.annotate('Fewer extreme \\n outliers', xy=(0.98, -17.3), xytext=(0, -12),\n",
    "            arrowprops=dict(facecolor='black'),\n",
    "            fontsize=14)\n",
    "\n",
    "# Feature V10\n",
    "sns.boxplot(x=\"Class\", y=\"V10\", data=new_df, ax=ax3, palette=colors)\n",
    "ax3.set_title(\"V10 Feature \\n Reduction of outliers\", fontsize=14)\n",
    "ax3.annotate('Fewer extreme \\n outliers', xy=(0.95, -16.5), xytext=(0, -12),\n",
    "            arrowprops=dict(facecolor='black'),\n",
    "            fontsize=14)\n",
    "\n",
    "\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfff = spark.createDataFrame(new_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.sql.functions import *\n",
    "from pyspark.sql.window import Window\n",
    "win = Window().orderBy('Time')\n",
    "dfff = dfff.withColumn(\"idx\", row_number().over(win))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Row(Time=Decimal('406.000000000000000000'), V1=-2.3122265423263, V2=1.95199201064158, V3=-1.60985073229769, V4=3.9979055875468, V5=-0.522187864667764, V6=-1.42654531920595, V7=-2.53738730624579, V8=1.39165724829804, V9=-2.77008927719433, V10=-2.77227214465915, V11=3.20203320709635, V12=-2.89990738849473, V13=-0.595221881324605, V14=-4.28925378244217, V15=0.389724120274487, V16=-1.14074717980657, V17=-2.83005567450437, V18=-0.0168224681808257, V19=0.416955705037907, V20=0.126910559061474, V21=0.517232370861764, V22=-0.0350493686052974, V23=-0.465211076182388, V24=0.320198198514526, V25=0.0445191674731724, V26=0.177839798284401, V27=0.261145002567677, V28=-0.143275874698919, Amount=0.0, Class=1, idx=1)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfff.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.ml import Pipeline\n",
    "from pyspark.ml.classification import GBTClassifier\n",
    "from pyspark.ml.feature import VectorIndexer, VectorAssembler\n",
    "from pyspark.ml.evaluation import BinaryClassificationEvaluator\n",
    "from pyspark.ml.linalg import DenseVector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Row(features=DenseVector([406.0, -2.3122, 1.952, -1.6099, 3.9979, -0.5222, -1.4265, -2.5374, 1.3917, -2.7701, -2.7723, 3.202, -2.8999, -0.5952, -4.2893, 0.3897, -1.1407, -2.8301, -0.0168, 0.417, 0.1269, 0.5172, -0.035, -0.4652, 0.3202, 0.0445, 0.1778, 0.2611, -0.1433]), label=1, index=1)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_df = dfff.rdd.map(lambda x: (DenseVector(x[0:29]),x[30],x[31]))\n",
    "\n",
    "\n",
    "training_df = spark.createDataFrame(training_df,[\"features\",\"label\",\"index\"])\n",
    "\n",
    "training_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "training_df = training_df.select(\"index\",\"features\",\"label\")\n",
    "\n",
    "train_data, test_data = training_df.randomSplit([.8,.2],seed=1234)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-----+-----+\n",
      "|label|count|\n",
      "+-----+-----+\n",
      "|    0|  408|\n",
      "|    1|  369|\n",
      "+-----+-----+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "train_data.groupBy(\"label\").count().show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-----+-----+\n",
      "|label|count|\n",
      "+-----+-----+\n",
      "|    0|   78|\n",
      "|    1|   88|\n",
      "+-----+-----+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "test_data.groupBy(\"label\").count().show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "gbt = GBTClassifier(featuresCol=\"features\", maxIter=100,maxDepth=8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = gbt.fit(train_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = model.transform(test_data)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+----------+-----+\n",
      "|prediction|count|\n",
      "+----------+-----+\n",
      "|       0.0|   85|\n",
      "|       1.0|   81|\n",
      "+----------+-----+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "predictions.groupBy(\"prediction\").count().show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9227855477855478"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "evaluator = BinaryClassificationEvaluator()\n",
    "evaluator.evaluate(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = predictions.withColumn(\"fraudPrediction\",when((predictions.label==1)&(predictions.prediction==1),1).otherwise(0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---------------+-----+\n",
      "|fraudPrediction|count|\n",
      "+---------------+-----+\n",
      "|              1|   73|\n",
      "|              0|   93|\n",
      "+---------------+-----+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "predictions.groupBy(\"fraudPrediction\").count().show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-----+-----+\n",
      "|label|count|\n",
      "+-----+-----+\n",
      "|    0|   78|\n",
      "|    1|   88|\n",
      "+-----+-----+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "predictions.groupBy(\"label\").count().show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.sql.functions import col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "82.95454545454545"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accurateFraud = predictions.groupBy(\"fraudPrediction\").count().where(predictions.fraudPrediction==1).head()[1]\n",
    "totalFraud = predictions.groupBy(\"label\").count().where(predictions.label==1).head()[1]\n",
    "FraudPredictionAccuracy = (accurateFraud/totalFraud)*100\n",
    "FraudPredictionAccuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "tp = predictions[(predictions.label == 1) & (predictions.prediction == 1)].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "tn = predictions[(predictions.label == 0) & (predictions.prediction == 0)].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "fp = predictions[(predictions.label == 0) & (predictions.prediction == 1)].count()\n",
    "fn = predictions[(predictions.label == 1) & (predictions.prediction == 0)].count()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True Positive:  73 \n",
      "True Negative:  70 \n",
      "False Positive:  8 \n",
      "False Negative:  15\n",
      "Recall:  0.8295454545454546\n",
      "Precision:  0.9012345679012346\n"
     ]
    }
   ],
   "source": [
    "print(\"True Positive: \",tp,\"\\nTrue Negative: \",tn,\"\\nFalse Positive: \",fp,\"\\nFalse Negative: \",fn)\n",
    "\n",
    "print(\"Recall: \",tp/(tp+fn))\n",
    "print(\"Precision: \", tp/(tp+fp))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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